RESUMEN
The quantification of an aerosol chemical composition is complicated by the uncertainty in the sensitivity of each species detected. Soft-ionization response factors can vary widely from molecule to molecule. Here, we have employed a method to separate molecules by their volatility through systematic evaporation with a thermal denuder (TD). The fraction remaining after evaporation is compared between an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) and a scanning mobility particle sizer (SMPS), which provides a comparison between a quantified mass loss by the SMPS and the signal loss in the EESI-TOF. The sensitivity of the EESI-TOF is determined for both a simplified complex mixture (PEG-300) and also for a complex mixture of α-pinene secondary organic aerosol (SOA). For PEG-300, separation is possible on a molecule-by-molecule level with the TD and provides insights into the molecule-dependent sensitivity of the EESI-TOF, showing a higher sensitivity toward the most volatile molecule. For α-pinene SOA, sensitivity determination for specific classes is possible because of the number of molecular formula observed by the EESI-TOF. These classes are separated by their volatility and are broken down into monomers (O3-5,6-7,8+), dimers (O4-7,8+), and higher order oligomers (e.g., trimers and tetramers). Here, we show that the EESI-TOF initially measures 60.1% monomers, 32.7% dimers, and 7.2% trimers and tetramers in α-pinene SOA, but after sensitivity correction, the distribution of SOA is 37.4% monomers, 56.1% dimers, and 6.4% trimers and tetramers. These results provide a path forward for the quantification of aerosol components with the EESI-TOF in other applications and potentially for atmospheric measurements.
RESUMEN
The first excited state of molecular oxygen is singlet-state oxygen (1O2), formed by indirect photochemistry of chromophoric organic matter. To determine whether 1O2 can be a competitive atmospheric oxidant, we must first quantify its production in organic aerosols (OA). Here, we report the spatiotemporal distribution of 1O2 over a 1-year dataset of PM10 extracts at two locations in Switzerland, representing a rural and suburban site. Using a chemical probe technique, we measured 1O2 steady-state concentrations with a seasonality over an order of magnitude peaking in wintertime at 4.59 ± 0.01 × 10-13 M and with a quantum yield of up to 2%. Next, we identified biomass burning and anthropogenic secondary OA (SOA) as the drivers for 1O2 formation in the PM10 aqueous extracts using source apportionment data. Importantly, the quantity, the amount of brown carbon present in PM10, and the quality, the chemical composition of the brown carbon present, influence the concentration of 1O2 sensitized in each extract. Anthropogenic SOA in the extracts were 4 times more efficient in sensitizing 1O2 than primary biomass burning aerosols. Last, we developed an empirical fit to estimate 1O2 concentrations based on PM10 components, unlocking the ability to estimate 1O2 from existing source apportionment data. Overall, 1O2 is likely a competitive photo-oxidant in PM10 since 1O2 is sensitized by ubiquitous biomass burning OA and anthropogenic SOA.