Linking Switzerland's PM10 and PM2.5 oxidative potential (OP) with emission sources

Particulate matter (PM) is the air pollutant that causes the greatest deleterious health effects across the world, so PM is routinely monitored within air quality networks, usually in respect to PM mass or number in different size fractions. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric that aims to classify PM in respect to its oxidising ability in the lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June 2018 and May 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations, which involved the quantification of a large number of PM constituents and the OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable over time and space: Bern-Bollwerk, an urban-traffic sampling site, had the greatest levels of OPv among the Swiss sites (especially when considering OPvAA ), with more rural locations such as Payerne experiencing a lower OPv. However, urban-background and suburban sites experienced a significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were 0.4-4.1 nmolmin(-1)m(-3), 0.6-3.0 nmolmin(-1)m(-3), and 0.3-0.7 nmolH(2)O(2)m(-3) for OPvAA, OPDvTT, and OPvDCFH, respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources that were identified contributed to OPv, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass) on average. A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with nonexhaust emissions, namely copper, zinc, iron, tin, antimony, manganese, and cadmium, as well as three specific wood-burning-sourced organic tracers - levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium), as the most important PM components to explain and predict OPv. The combination of a metal and a wood-burning-specific tracer led to the best-performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical; the models simply required a variable representing the emission source or process to be present. This analysis strongly suggests that anthropogenic and locally emitting road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv and presumably biological harm reductions in Switzerland.