Articles | Volume 24, issue 12 
            
                
                    
            
            
            https://doi.org/10.5194/acp-24-7261-2024
                    © Author(s) 2024. 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-24-7261-2024
                    © Author(s) 2024. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Unveiling the optimal regression model for source apportionment of the oxidative potential of PM10
Vy Dinh Ngoc Thuy
                                            Université Grenoble Alpes, CNRS, IRD, INP-G, INRAE, IGE (UMR 5001), 38000 Grenoble, France
                                        
                                    Jean-Luc Jaffrezo
                                            Université Grenoble Alpes, CNRS, IRD, INP-G, INRAE, IGE (UMR 5001), 38000 Grenoble, France
                                        
                                    Ian Hough
                                            Université Grenoble Alpes, CNRS, IRD, INP-G, INRAE, IGE (UMR 5001), 38000 Grenoble, France
                                        
                                    Pamela A. Dominutti
                                            Université Grenoble Alpes, CNRS, IRD, INP-G, INRAE, IGE (UMR 5001), 38000 Grenoble, France
                                        
                                    Guillaume Salque Moreton
                                            Atmo AuRA, 69500 Bron, France
                                        
                                    Grégory Gille
                                            Atmo Sud, 13006 Marseille, France
                                        
                                    Florie Francony
                                            Atmo Nouvelle Aquitaine, 33692 Merignac, France
                                        
                                    Arabelle Patron-Anquez
                                            Atmo Hauts de France, 59044 Lille, France
                                        
                                    Olivier Favez
                                            INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
                                        
                                    
                                            Laboratoire central de surveillance de la qualité de l'air (LCSQA), 60550 Verneuil-en-Halatte, France
                                        
                                    
                                            Université Grenoble Alpes, CNRS, IRD, INP-G, INRAE, IGE (UMR 5001), 38000 Grenoble, France
                                        
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                            Cited
9 citations as recorded by crossref.
- Assessment of oxidative stress induced by atmospheric particulate matter: from acellular and cellular assays to the use of model and experimental organisms E. Vaccarella et al. 10.1016/j.scitotenv.2025.178651
- The role of TPBF, perceived moral obligation and green intention on pro-environmental behavior and environmental sustainability of wetland MSMEs with an environmental-based view approach for green business M. Naparin 10.1080/23311886.2025.2488115
- Dithiothreitol oxidative potential (OP) of PM2.5 in Beijing: Quantitative contribution of metals to OP and its prediction based on machine learning models Y. Ma et al. 10.1016/j.jhazmat.2025.139471
- Comparison of modelled and experimental PM10 source contributions for mapping source-specific oxidative potential F. Pekel et al. 10.1016/j.aeaoa.2025.100339
- Oxidative potential of atmospheric particles in Europe and exposure scenarios C. Tassel et al. 10.1038/s41586-025-09666-9
- Explainable AI for predicting oxidative potential of fine particles and key chemical drivers S. Lee et al. 10.1016/j.jhazmat.2025.139842
- A comparative study of methods for calculating the oxidative potential (OP) of atmospheric particulate matter E. Souza et al. 10.1039/D5EA00025D
- Chemometric assessment, seasonal variation and source apportionment of air pollutants in Islamabad's industrial area M. Anjum et al. 10.1016/j.jtemin.2025.100244
- Oxidative potential and cellular toxicity of carbonaceous aerosols undergoing aging in an atmospheric simulation chamber V. Vernocchi et al. 10.1016/j.taap.2025.117573
9 citations as recorded by crossref.
- Assessment of oxidative stress induced by atmospheric particulate matter: from acellular and cellular assays to the use of model and experimental organisms E. Vaccarella et al. 10.1016/j.scitotenv.2025.178651
- The role of TPBF, perceived moral obligation and green intention on pro-environmental behavior and environmental sustainability of wetland MSMEs with an environmental-based view approach for green business M. Naparin 10.1080/23311886.2025.2488115
- Dithiothreitol oxidative potential (OP) of PM2.5 in Beijing: Quantitative contribution of metals to OP and its prediction based on machine learning models Y. Ma et al. 10.1016/j.jhazmat.2025.139471
- Comparison of modelled and experimental PM10 source contributions for mapping source-specific oxidative potential F. Pekel et al. 10.1016/j.aeaoa.2025.100339
- Oxidative potential of atmospheric particles in Europe and exposure scenarios C. Tassel et al. 10.1038/s41586-025-09666-9
- Explainable AI for predicting oxidative potential of fine particles and key chemical drivers S. Lee et al. 10.1016/j.jhazmat.2025.139842
- A comparative study of methods for calculating the oxidative potential (OP) of atmospheric particulate matter E. Souza et al. 10.1039/D5EA00025D
- Chemometric assessment, seasonal variation and source apportionment of air pollutants in Islamabad's industrial area M. Anjum et al. 10.1016/j.jtemin.2025.100244
- Oxidative potential and cellular toxicity of carbonaceous aerosols undergoing aging in an atmospheric simulation chamber V. Vernocchi et al. 10.1016/j.taap.2025.117573
Latest update: 31 Oct 2025
Short summary
                    The capacity of particulate matter (PM) to generate reactive oxygen species in vivo is represented by oxidative potential (OP). This study focuses on finding the appropriate model to evaluate the oxidative character of PM sources in six sites using the PM sources and OP. Eight regression techniques are introduced to assess the OP of PM. The study highlights the importance of selecting a model according to the input data characteristics and establishes some recommendations for the procedure.
                    The capacity of particulate matter (PM) to generate reactive oxygen species in vivo is...
                    
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