Atmospheric conditions and composition that influence PM 2.5 oxidative potential in Beijing, China

Abstract. Epidemiological studies have consistently linked exposure to PM2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP, however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity (APHH-Beijing) campaign, and PM2.5 OP was analysed using four acellular methods; ascorbic acid (AA), dithiothreitol (DTT), 2-7-dichlorofluoroscin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Positive correlations of OP normalised per volume of air of all four assays with overall PM2.5 mass was observed, with stronger correlations in the winter compared to the summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM2.5 mass concentrations (μg m−3) were found to have lower intrinsic mass-normalised OP values as measured by AA and DTT. This indicates that total PM2.5 mass concentrations alone might not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM2.5 composition, gas phase composition and meteorological data, provides detailed insight into chemical components or atmospheric processes that determine PM2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM2.5 for both mass- and volume-normalised data. Variable selection was used to produce subsets of measurements indicative of PM2.5 sources, and used to model OP response; AA and DTT assays were well predicted by small panels of measurements, and indicated fossil fuel combustion processes, vehicle emissions and biogenic SOA as most influential in the assay response. Through comparative analysis of both mass- and volume-normalised data we demonstrate the importance of also considering mass-normalised OP when correlating with particle composition measurements, which provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis, and which may be more useful in temporal and site comparative contexts.