Many studies have demonstrated associations between exposure to ambient particulate matter (PM) and adverse health outcomes in humans that can be explained by PM capacity to induce oxidative stress in vivo. Thus, assays have been developed to quantify the oxidative potential (OP) of PM as a more refined exposure metric than PM mass alone. Only a small number of studies have compared different acellular OP measurements for a given set of ambient PM samples. Yet, fewer studies have compared different assays over a year-long period and with detailed chemical characterization of ambient PM. In this study, we report on seasonal variations of the dithiothreitol (DTT), ascorbic acid (AA), electron spin resonance (ESR) and the respiratory tract lining fluid (RTLF, composed of the reduced glutathione (GSH) and ascorbic acid (ASC)) assays over a 1-year period in which 100 samples were analyzed. A detailed PM₁₀ characterization allowed univariate and multivariate regression analyses in order to obtain further insight into groups of chemical species that drive OP measurements. Our results show that most of the OP assays were strongly intercorrelated over the sampling year but also these correlations differed when considering specific sampling periods (cold vs. warm). All acellular assays are correlated with a significant number of chemical species when considering univariate correlations, especially for the DTT assay. Evidence is also presented of a seasonal contrast over the sampling period with significantly higher OP values during winter for the DTT, AA, GSH and ASC assays, which were assigned to biomass burning species by the multiple linear regression models. The ESR assay clearly differs from the other tests as it did not show seasonal dynamics and presented weaker correlations with other assays and chemical species.

In order to identify and quantify key species associated with non-exhaust emissions and exhaust vehicular emissions, a large comprehensive dataset of particulate species has been obtained thanks to simultaneous near-road and urban background measurements coupled with detailed traffic counts and chassis dynamometer measurements of exhaust emissions of a few in-use vehicles well-represented in the French fleet. Elemental carbon, brake-wear metals (Cu, Fe, Sb, Sn, Mn), n-alkanes (C19-C26), light-molecular-weight polycyclic aromatic hydrocarbons (PAHs; pyrene, fluoranthene, anthracene) and two hopanes (17α21βnorhopane and 17α21βhopane) are strongly associated with the road traffic. Traffic-fleet emission factors have been determined for all of them and are consistent with most recent published equivalent data. When possible, light-duty- and heavy-duty-traffic emission factors are also determined. In the absence of significant non-combustion emissions, light-duty-traffic emissions are in good agreement with emissions from chassis dynamometer measurements. Since recent measurements in Europe including those from this study are consistent, ratios involving copper (Cu∕Fe and Cu∕Sn) could be used as brake-wear emissions tracers as long as brakes with Cu remain in use. Near the Grenoble ring road, where the traffic was largely dominated by diesel vehicles in 2011 (70 %), the OC∕EC ratio estimated for traffic emissions was around 0.4. Although the use of quantitative data for source apportionment studies is not straightforward for the identified organic molecular markers, their presence seems to well-characterize fresh traffic emissions.

Inhaled aerosolized particulate matter (PM) induces cellular oxidative stress in vivo, leading to adverse health outcomes. The oxidative potential (OP) of PM appears to be a more relevant proxy of the health impact of the aerosol rather than the total mass concentration. However, the relative contributions of the aerosol sources to the OP are still poorly known. In order to better quantify the impact of different PM sources, we sampled aerosols in a French city for one year (2014, 115 samples). A coupled analysis with detailed chemical speciation (more than 100 species, including organic and carbonaceous compounds, ions, metals and aethalometer measurements) and two OP assays (ascorbic acid, AA, and dithiothreitiol, DTT) in a simulated lung fluid (SLF) were performed in these samples. We present in this study a statistical framework using a coupled approach with positive matrix factorization (PMF) and multiple linear regression to attribute a redox-activity to PM sources. Our results highlight the importance of the biomass burning and vehicular sources to explain the observed OP for both assays. In general, we see a different contribution of the sources when considering the OP AA, OP DTT or the mass of the PM₁₀. Moreover, significant differences are observed between the DTT and AA tests which emphasized chemical specificities of the two tests and the need of a standardized approach for the future studies on epidemiology or toxicology of the PM.