Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry

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.

A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometry

“…A tempting alternative to the method presented here (combining SPMS data with coincident size distribution measurements) is to determine the SPMS particle detection efficiency as a function of size under controlled conditions, and then multiply this curve by the airborne size-dependent data rate to yield a quantitative particle concentration, similar to SPMS scaling methods used at ground sites (Bein et al, 2006;Jeong et al, 2011;Pratt et al, 2009b;Shen et al, 2019). However, this approach is not recommended due to many possible pitfalls and large, unquantifiable errors.…”

“…Relative abundance measurements of internally mixed aerosol sub-components have been reported for metals (Cziczo et al, 2001;Healy et al, 2013;Murphy et al, 2007;Zawadowicz et al, 2015), organosulfate species (Froyd et al, 2010;Liao et al, 2015), elemental carbon (EC; Healy et al, 2012), and non-refractory material such as ammonium and nitrate (Healy et al, 2013), or sulfate and organic material (Healy et al, 2013;Jeong et al, 2011;Middlebrook et al, 1998;Murphy et al, 2006;Zelenyuk et al, 2008;Zhou et al, 2016). Some groups have scaled SPMS data rates to aerosol reference instruments, either under controlled conditions prior to deployment (Shen et al, 2019) or more commonly co-located in the field, to derive total number or mass concentrations (Bein et al, 2006;Pratt et al, 2009a;Qin et al, 2006) or concentrations for specific particle types and sub-components (Gemayel et al, 2017;Healy et al, 2012Healy et al, , 2013Jeong et al, 2011;Reinard et al, 2007;Shen et al, 2019). Many of these scaling studies invoke potentially large assumptions such as constant SPMS detection efficiencies or a single density applied to all particles that can strongly affect derived concentrations.…”

“…Many of these scaling studies invoke potentially large assumptions such as constant SPMS detection efficiencies or a single density applied to all particles that can strongly affect derived concentrations. Uncertainty estimates in these derived concentrations are rarely reported (Shen et al, 2019), and a complete analysis of all principal error sources has not been previously undertaken. To date these methods have been restricted to ground-based sampling under relatively high aerosol loadings (∼ 1-100 µg m −3 ) and have employed long sample times ≥ 1 h. Few coarse-mode concentrations have been reported (Gunsch et al, 2018;Qin et al, 2006), and in particular, studies using SPMS to determine absolute concentrations of mineral dust are nearly absent (Shen et al, 2019).…”

“…Also, instruments and aircraft inlets must be optimized to sample coarse-mode aerosol up to several microns in size. Electron microscopy (EM) techniques with associated elemental analysis remain valuable offline single-particle methods to detect and quantify components associated with mineral dust (Kandler et al, 2009;Levin et al, 2005;Lieke et al, 2011;Matsuki et al, 2010) and other less volatile aerosol such as sulfate, sea salt, industrial metals, and some carbonaceous particles (Pósfai et al, 2003;Sheridan et al, 1994). Computer-controlled EM analysis can now characterize thousands of particles and generate population statistics of size, morphology, and detailed chemical composition (Ault and Axson, 2017;Craig et al, 2017).…”

Resonance-Enhanced Detection of Metals in Aerosols using Single Particle Mass Spectrometry

“…Several poorly determined interactions at the particle surface and in the desorbed plume affect ion formation (Hatch et al, 2014;Hinz and Spengler, 2007;Murphy, 2007;Reilly et al, 2000;Reinard and Johnston, 2008;Schoolcraft et al, 2000;70 Wade et al, 2008), reduce detection efficiencies and complicate quantification approaches (Fergenson et al, 2001;Gemayel et al, 2017;Gross et al, 2000;Healy et al, 2013;Qin et al, 2006;Shen et al, 2019;Zhou et al, 2016). These difficulties can be mitigated if the desorption and ionization are separated in a two-step process, and ions are formed by resonant ionization in the gaseous plume, as demonstrated for aromatic hydrocarbons (Bente et al, 2008;Morrical et al, 1998;Woods et al, 2001).…”