Organic aerosol source apportionment in Zurich using an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS) – Part 2: Biomass burning influences in winter
Real-time, in situ molecular composition measurements of the organic fraction of fine particulate matter (PM2.5) remain challenging, hindering a full understanding of the climate impacts and health effects of PM2.5. In particular, the thermal decomposition and ionization-induced fragmentation affecting current techniques has limited a detailed investigation of secondary organic aerosol (SOA), which typically dominates OA. Here we deploy a novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS) during winter 2017 in downtown Zurich, Switzerland, which overcomes these limitations, together with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and supporting instrumentation. Positive matrix factorization (PMF) implemented within the Multilinear Engine (ME-2) program was applied to the EESI-TOF-MS data to quantify the primary and secondary contributions to OA. An 11-factor solution was selected as the best representation of the data, including five primary and six secondary factors. Primary factors showed influence from cooking, cigarette smoke, biomass burning (two factors) and a special local unknown event occurred only during two nights. Secondary factors were affected by biomass burning (three factors, distinguished by temperature and/or wind direction), organonitrates, monoterpene oxidation, and undetermined regional processing, in particular the contributions of wood combustion. While the AMS attributed slightly over half the OA mass to SOA but did not identify its source, the EESI-TOF-MS showed that most (>70 %) of the SOA was derived from biomass burning. Together with significant contributions from less aged biomass burning factors identified by both AMS and EESI-TOF-MS, this firmly establishes biomass burning as the single most important contributor to OA mass at this site during winter. High correlation was obtained between EESI-TOF-MS and AMS PMF factors where specific analogues existed, as well as between total signal and POA–SOA apportionment. This suggests the EESI-TOF-MS apportionment in the current study can be approximately taken at face value, despite ion-by-ion differences in relative sensitivity. The apportionment of specific ions measured by the EESI-TOF-MS (e.g., levoglucosan, nitrocatechol, and selected organic acids) and utilization of a cluster analysis-based approach to identify key marker ions for the EESI-TOF-MS factors are investigated. The interpretability of the EESI-TOF-MS results and improved source separation relative to the AMS within this pilot campaign validate the EESI-TOF-MS as a promising approach to source apportionment and atmospheric composition research.