Preprints
https://doi.org/10.5194/acp-2021-57
https://doi.org/10.5194/acp-2021-57

  10 Feb 2021

10 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal ACP.

Disparities in particulate matter (PM10) origins and oxidative potential at a city-scale (Grenoble, France) – Part II: Sources of PM10 oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis

Lucille Joanna S. Borlaza1, Samuël Weber1, Jean-Luc Jaffrezo1, Stephan Houdier1, Rémy Slama2, Camille Rieux3, Alexandre Albinet4, Steve Micallef3, Cécile Trébluchon3, and Gaëlle Uzu1 Lucille Joanna S. Borlaza et al.
  • 1University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), F-38000 Grenoble, France
  • 2University of Grenoble Alpes, Inserm, CNRS, IAB (Institute of Advanced Biosciences), Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
  • 3Atmo AuRA, F-38400 Grenoble, France
  • 4INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

Abstract. The oxidative potential (OP) of particulate matter (PM) quantifies PM capability to cause anti-oxidant imbalance. Due to the wide range and complex mixture of species in particulates, little is known on the pollution sources most strongly contributing to OP. A one-year sampling of PM10 (particles with an aerodynamic diameter below 10) was performed over different sites in a medium-sized city (Grenoble, France). An enhanced fine-scale apportionment of PM10 sources, based on the chemical composition, was performed using Positive Matrix Factorization (PMF) method and reported in a companion paper (Borlaza et al., 2020). OP was assessed as the ability of PM10 to generate reactive oxygen species (ROS) using three different acellular assays: Dithiothreitol (DTT), Ascorbic acid (AA), and 2,7-dichlorofluorescein (DCFH) assays. Using multiple linear regression (MLR), the OP contribution of the sources identified by PMF were estimated. Conversely, since atmospheric processes are usually non-linear in nature, artificial neural network (ANN) techniques, which employs non-linear models, could further improve estimates. Hence, the multilayer perceptron analysis (MLP), an ANN-based model, was additionally used to model OP based on PMF-resolved sources as well. This study presents the spatiotemporal variabilities of OP activity with influences by season-specific sources, site typology and specific local features, and assay sensitivity. Overall, both MLR and MLP effectively captured the evolution of OP. The primary traffic and biomass burning sources were the strongest drivers of OP in the Grenoble basin. There is also a clear redistribution of source-specific impacts when using OP instead of mass concentration, underlining the importance of PM redox activity over mass concentration. Finally, the MLP generally offered improvements in OP prediction especially for sites where synergistic and/or antagonistic effects between sources are prominent, supporting the value of using ANN-based models to account for the non-linear dynamics behind the atmospheric processes affecting OP of PM10.

Lucille Joanna S. Borlaza et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-57', Anonymous Referee #1, 10 Apr 2021
  • RC2: 'Comment on acp-2021-57', Anonymous Referee #2, 15 Apr 2021
  • AC1: 'Comment on acp-2021-57', Lucille Joanna Borlaza, 26 Apr 2021

Lucille Joanna S. Borlaza et al.

Lucille Joanna S. Borlaza et al.

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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 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.
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