Comment on acp-2021-979

Abstract

Particulate matter (PM) is the air pollutant which causes the greatest deleterious heath effects across the world and PM is routinely monitored within air quality networks where PM mass according to its size, and sometimes number are reported. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric which aims to classify PM in respect to its oxidising ability in 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 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 in time and space with Bern-Bollwerk, an urban-traffic sampling site having the greatest levels of OPv among the Swiss sites (especially when considering ), with more rural locations such as Payerne experiencing lower OPv. However, urban-background and suburban sites did experience 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, 0.6–3.0, and 0.3–0.7 nmol min−1 m−3 for the , , and respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources which were identified contributed to OPv on average, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass). A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely: copper, zinc, iron, tin, antimony and somewhat manganese and cadmium as well as three specific wood burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium) were 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 to be present to represent the emission source or process. The modelling process also showed that may be a more specific metric for OP than the other assays employed in this work. This analysis strongly suggests that the anthropogenic and locally emitted 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.