Abstract
Abstract. Single-particle mass spectrometry (SPMS) is a widely used tool to determine chemical composition and mixing state of aerosol particles in the atmosphere. During a 6-week field campaign in summer 2016 at a rural site in the upper Rhine valley, near the city of Karlsruhe in southwest Germany, ∼3.7×105 single particles were analysed using a laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF). Combining fuzzy classification, marker peaks, typical peak ratios, and laboratory-based reference spectra, seven major particle classes were identified. With the precise particle identification and well-characterized laboratory-derived overall detection efficiency (ODE) for this instrument, particle similarity can be transferred into corrected number and mass fractions without the need of a reference instrument in the field. Considering the entire measurement period, aged-biomass-burning and soil-dust-like particles dominated the particle number (45.0 % number fraction) and mass (31.8 % mass fraction); sodium-salt-like particles were the second lowest in number (3.4 %) but the second dominating class in terms of particle mass (30.1 %). This difference demonstrates the crucial role of particle number counts' correction for mass quantification using SPMS data. Using corrections for size-resolved and chemically resolved ODE, the total mass of the particles measured by LAAPTOF accounts for 23 %–68 % of the total mass measured by an aerosol mass spectrometer (AMS) depending on the measurement periods. These two mass spectrometers show a good correlation (Pearson's correlation coefficient γ>0.6) regarding total mass for more than 85 % of the measurement time, indicating non-refractory species measured by AMS may originate from particles consisting of internally mixed non-refractory and refractory components. In addition, specific relationships of LAAPTOF ion intensities and AMS mass concentrations for non-refractory compounds were found for specific measurement periods, especially for the fraction of org ∕ (org + nitrate). Furthermore, our approach allows the non-refractory compounds measured by AMS to be assigned to different particle classes. Overall AMS nitrate mainly arose from sodium-salt-like particles, while aged-biomass-burning particles were dominant during events with high organic aerosol particle concentrations.
Highlights
Lifetimes of ambient aerosol particles range from hours to several days, except for newly formed particles (∼ 3 to 5 nm), which have a lifetime on the order of seconds (Pöschl, 2005)
Using corrections for size-resolved and chemically resolved overall detection efficiency (ODE), the total mass of the particles measured by laser ablation aerosol particle time-of-flight mass spectrometer (LAAPTOF) accounts for 23 %–68 % of the total mass measured by an aerosol mass spectrometer (AMS) depending on the measurement periods
Signatures for organic and secondary inorganic compounds can be observed in each class, i.e. for organics m/z 24 C−2, 25 C2H−, 26 C2H2/CN−, and 42 C2H2O/CNO−, for sulfate 32 S−, 64 SO−2, 80 SO−3, 81 HSO−3, 97 HSO−4, 177 SO3HSO−4, and 195 HSO4H2SO−4, for nitrate 30 NO+, 46 NO−2, and 62 NO−3, and for ammonium 18 NH+4 and 30 NO+
Summary
Lifetimes of ambient aerosol particles range from hours to several days, except for newly formed particles (∼ 3 to 5 nm), which have a lifetime on the order of seconds (Pöschl, 2005). The atmospheric evolution of aerosol particles can alter their internal and external mixing states, as well as their chemical and physical properties on timescales of several hours; e.g. X. Most aerosol particles are relatively complex mixtures; they are not easy to distinguish and trace to their primary source and/or secondary formation pathway. Single-particle mass spectrometry (SPMS) has the capability of measuring most components of the particles in real time; it has been a widely used technique to investigate mixing state and aging of aerosol particles for many years (Murphy, 2007; Noble and Prather, 2000; Pratt and Prather, 2012). There are still challenging issues related to large amounts of SPMS data analysis
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