Source apportionment of atmospheric PM<sub>10</sub> oxidative potential: synthesis of 15 year-round urban datasets in France

Abstract

Abstract. Reactive oxygen species (ROS) carried or induced by particulate matter (PM) are suspected of inducing oxidative stress in vivo, leading to adverse health impacts such as respiratory or cardiovascular diseases. The oxidative potential (OP) of PM, displaying the ability of PM to oxidize the lung environment, is gaining strong interest in examining health risks associated with PM exposure. In this study, OP was measured by two different acellular assays (dithiothreitol, DTT, and ascorbic acid, AA) on PM10 filter samples from 15 yearly time series of filters collected at 14 different locations in France between 2013 and 2018, including urban, traffic and Alpine valley site typologies. A detailed chemical speciation was also performed on the same samples, allowing the source apportionment of PM using positive matrix factorization (PMF) for each series, for a total number of more than 1700 samples. This study then provides a large-scale synthesis of the source apportionment of OP using coupled PMF and multiple linear regression (MLR) models. The primary road traffic, biomass burning, dust, MSA-rich, and primary biogenic sources had distinct positive redox activity towards the OPDTT assay, whereas biomass burning and road traffic sources only display significant activity for the OPAA assay. The daily median source contribution to the total OPDTT highlighted the dominant influence of the primary road traffic source. Both the biomass burning and the road traffic sources contributed evenly to the observed OPAA. Therefore, it appears clear that residential wood burning and road traffic are the two main target sources to be prioritized in order to decrease significantly the OP in western Europe and, if the OP is a good proxy of human health impact, to lower the health risks from PM exposure.

9-year trends of PM<sub>10 </sub>sources and oxidative potential in a rural background site in France

“…For 𝑂𝑃 𝑣 𝐴𝐴 , the biomass burning factor showed the highest contribution (0.12 nmol min⁻¹ m⁻ 3 ), followed by the traffic (0.07 nmol min⁻¹ m⁻ 3 ) and nitrate-rich (0.06 nmol min⁻¹ m⁻ 3 ) factors. Although lower in magnitude, the OP contribution of mineral dust, traffic, and biomass burning (only in 𝑂𝑃 𝑣 𝐴𝐴 ) are also prominent in the OPE site, similar to other sites in France (Weber et al, 2021). These sources are commonly composed of 445 species that are highly redox-active, hence it is not surprising that they are one of the main drivers of OP even in a rural site.…”

“…In many European studies, the seasonality in PM10 mass concentration can be usually explained by higher contributions from biomass burning during winter (Bessagnet et al, 2020;Tomaz et al, 2017), especially in alpine valleys (Calas et al, 2019b;Favez et al, 2010;Herich et al, 2014;Srivastava et al, 2018;Tomaz et al, 2016Tomaz et al, , 2017Weber et al, 2018Weber et al, , 2019. Similarly, this seasonal pattern has been observed in OP as well (Borlaza et al, 2021;Weber et al, 2018;Calas et al, 2019b;Weber et al, 2021). However, the typology (i.e., rural) of OPE site could be associated with a different type of OP temporal profile as it is far from direct anthropogenic emission sources (but not from vegetation and soil biogenic emissions).…”

“…The OP contribution of each PM10 source was determined by performing an OP deconvolution method using multiple linear regression (MLR) analysis. This method is based on previous reports on OP studies in France (Weber et al, 2018(Weber et al, , 2021Borlaza et al, 2021). Briefly, the OP activity in nmol/min/m³ was used as the dependent variable, while the PMF-resolved source PM10 mass contributions in µg/m³ are the independent variables, as shown in Eq.…”

“…Figure 7 presents the observed average values of OP of PM10 in the OPE site compared to the other sites in France (Calas et al, , 2019Weber et al, 2021Weber et al, , 2018. All series cover at least one year of sampling.…”

“…The OP assay sensitivity to specific species and/or sources can also explain the difference in seasonality found in 𝑂𝑃 𝑣 𝐷𝑇𝑇 and 𝑂𝑃 𝑣 𝐴𝐴 in our study. 𝑂𝑃 𝑣 𝐷𝑇𝑇 appears sensitive towards organics, metals, and, possibly, a synergistic effect between the two (Bates et al, 2019;Dou et al, 2015;Fang et al, 2017;Gao et al, 2020b, a;Jiang et al, 2019;Weber et al, 2021;Yu et al, 2018;Borlaza et al, 2021), while, 𝑂𝑃 𝑣 𝐴𝐴 shows sensitivity mostly towards metal species (Bates et al, 2019;Crobeddu et al, 2017;Visentin et al, 2016;Weber et al, 2021;Borlaza et al, 2021).…”