气候变化领域集成服务门户(CCPortal): 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

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DOI	10.5194/acp-21-9719-2021
	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
	Borlaza L.J.S.; Weber S.; Jaffrezo J.-L.; Houdier S.; Slama R.; Rieux C.; Albinet A.; Micallef S.; Trébluchon C.; Uzu G.
发表日期	2021
ISSN	1680-7316
起始页码	9719
结束页码	9739
卷号	21 期号:12
英文摘要	The oxidative potential (OP) of particulate matter (PM) measures PM capability to potentially cause anti-oxidant imbalance. Due to the wide range and complex mixture of species in particulates, little is known about the pollution sources most strongly contributing to OP. A 1-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 the 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 contributions of the sources identified by PMF were estimated. Conversely, since atmospheric processes are usually non-linear in nature, artificial neural network (ANN) techniques, which employ 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 for the identification of potential sources of PM toxicity. 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. © 2021 Lucille Joanna S. Borlaza et al.
语种	英语
scopus关键词	artificial neural network; multiple regression; particulate matter; reactive oxygen species; spatial variation; temporal variation; Auvergne-Rhone-Alpes; France; Grenoble; Isere
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/246776
作者单位	University of Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), Grenoble, 38000, France; University of Grenoble Alpes, Inserm, CNRS, IAB (Institute of Advanced Biosciences), Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Grenoble, France; Atmo AuRA, Grenoble, 38400, France; INERIS, Parc Technologique Alata, BP 2, Verneuil-en-Halatte, 60550, France
推荐引用方式 GB/T 7714	Borlaza L.J.S.,Weber S.,Jaffrezo J.-L.,et al. 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[J],2021,21(12).
APA	Borlaza L.J.S..,Weber S..,Jaffrezo J.-L..,Houdier S..,Slama R..,...&Uzu G..(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.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(12).
MLA	Borlaza L.J.S.,et al."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".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.12(2021).