Abstract
Abstract. 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.
Highlights
One of the most critical pollutants in the atmosphere is particulate matter (PM), especially in urban areas that are heavily impacted by anthropogenic emissions
The daily distributions of PM10 and oxidative potential (OP) activity (OPDv TT, OPAv A, and OPDv CFH) for each site are provided in Sect
Such observed seasonality in PM10 mass concentration is commonly explained by higher contributions from the biomass burning source in the colder seasons, especially in an Alpine valley as previously reported in previous studies (Calas et al, 2019; Favez et al, 2010; Herich et al, 2014; Srivastava et al, 2018; Tomaz et al, 2016, 2017; Weber et al, 2018, 2019)
Summary
One of the most critical pollutants in the atmosphere is particulate matter (PM), especially in urban areas that are heavily impacted by anthropogenic emissions The intricate topography and seasonality of particulate air pollution in the city of Grenoble (France) make it an ideal location to explore variabilities of PM pollution while accounting for different site typologies within a single medium-sized city (Calas et al, 2019; Favez et al, 2010; Srivastava et al, 2018; Tomaz et al, 2016, 2017; Weber et al, 2019) Such small-scale variabilities for mass and chemical composition have been recently addressed in a companion paper (Borlaza et al, 2021).
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