Quantification of primary and secondary organic aerosol sources by combined factor analysis of extractive electrospray ionisation and aerosol mass spectrometer measurements (EESI-TOF and AMS)

Abstract. Source apportionment studies have struggled to quantitatively link secondary organic aerosols (SOAs) to their precursor sources due largely to instrument limitations. For example, aerosol mass spectrometer (AMS) provides quantitative measurements of the total SOA fraction but lacks the chemical resolution to resolve most SOA sources. In contrast, instruments based on soft ionisation techniques, such as extractive electrospray ionisation mass spectrometry (EESI, e.g. the EESI time-of-flight mass spectrometer, EESI-TOF), have demonstrated the resolution to identify specific SOA sources but provide only a semi-quantitative apportionment due to uncertainties in the dependence of instrument sensitivity on molecular identity. We address this challenge by presenting a method for positive matrix factorisation (PMF) analysis on a single dataset which includes measurements from both AMS and EESI-TOF instruments, denoted “combined PMF” (cPMF). Because each factor profile includes both AMS and EESI-TOF components, the cPMF analysis maintains the source resolution capability of the EESI-TOF while also providing quantitative factor mass concentrations. Therefore, the bulk EESI-TOF sensitivity to each factor can also be directly determined from the analysis. We present metrics for ensuring that both instruments are well represented in the solution, a method for optionally constraining the profiles of factors that are detectable by one or both instruments, and a protocol for uncertainty analysis. As a proof of concept, the cPMF analysis was applied to summer and winter
measurements in Zurich, Switzerland. Factors related to biogenic and wood-burning-derived SOAs are quantified, as well as POA sources such as wood
burning, cigarette smoke, cooking, and traffic. The retrieved EESI-TOF
factor-dependent sensitivities are consistent with both laboratory measurements of SOA from model precursors and bulk sensitivity parameterisations based on ion chemical formulae. The cPMF analysis shows
that, with the standalone EESI-TOF PMF, in which factor-dependent
sensitivities are not accounted for, some factors are significantly
under- or overestimated. For example, when factor-dependent sensitivities are
not considered in the winter dataset, the SOA fraction is underestimated by
∼25 % due to the high EESI-TOF sensitivity to components of primary biomass burning such as levoglucosan. In the summer dataset, where
both SOA and total OA are dominated by monoterpene oxidation products, the
uncorrected EESI-TOF underestimates the fraction of daytime SOA relative to
nighttime SOA (in which organonitrates and less oxygenated
CxHyOz molecules are enhanced). Although applied here to an
AMS and EESI-TOF pairing, cPMF is suitable for the general case of a
multi-instrument dataset, thereby providing a framework for exploiting
semi-quantitative, high-resolution instrumentation for quantitative source
apportionment.
