Comparison between five acellular oxidative potential measurement assays performed with detailed chemistry on PM<sub>10</sub> samples from the city of Chamonix (France)

Abstract

Abstract. 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 15 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 one year period in which 100 samples were analysed. A detailed PM 10 characterization allowed univariate and 20 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 inter-correlated 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 25 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.Atmos. Chem. Phys. Discuss., https://doi

Oxidative stress-induced inflammation in susceptible airways by anthropogenic aerosol

“…The PM solutions were not filtered to also allow surface reactivity of the PM. The depletion was followed during 30 min with a plate-reader using the absorbance mode at 265 nm for the AA assay, at 412 nm for the DTT assay (titration of DTT by 5,5-dithio-bis-(2-nitrobenzoic acid) DTNB) according to Calas et al [35]. The oxidation of DCFH into the fluorescent compound DCF was assessed by fluorescence at 530 nm for emission (excitation at 485 nm) according to Foucaud et al [36].…”

An apportionment method for the Oxydative Potential to the atmospheric PM sources: application to a one-year study in Chamonix, France

“…been conducted (Chevrier, 2016), and the OP measurements performed on the same samples (Calas et al, 2018). These are briefly presented below. )…”

Spatial variations in the estimated production of reactive oxygen species in the epithelial lung lining fluid by iron and copper in fine particulate air pollution

“…To date, existing evidence suggests that ROS generation estimated using Fe and Cu concentrations in the KM-SUB-ELF model is at least moderately correlated with these metrics. 28 – 31 For example, a recent evaluation of five oxidative potential metrics for PM 10 samples reported correlations for Cu and the above oxidative potential assays ranging from 0.48 to 0.87 with a similar range of correlations observed for Fe (0.48–0.71). 31 Ultimately, the more important question is which of these assays is the best predictor of adverse health effects and which (if any) are superior to particle mass concentration in this respect.…”

“… 28 – 31 For example, a recent evaluation of five oxidative potential metrics for PM 10 samples reported correlations for Cu and the above oxidative potential assays ranging from 0.48 to 0.87 with a similar range of correlations observed for Fe (0.48–0.71). 31 Ultimately, the more important question is which of these assays is the best predictor of adverse health effects and which (if any) are superior to particle mass concentration in this respect. At a minimum, the KM-SUB-ELF model may serve as a cost-effective means of estimating the oxidative potential of airborne particles when Fe and Cu data are available in the absence of filter media (e.g., historical data) or when detailed chemical analyses of filter samples are not possible.…”