scholarly journals Single-particle mass spectrometry of tropospheric aerosol particles

2006 ◽  
Vol 111 (D23) ◽  
Author(s):  
D. M. Murphy ◽  
D. J. Cziczo ◽  
K. D. Froyd ◽  
P. K. Hudson ◽  
B. M. Matthew ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Particle ◽  
Aerosol Particles ◽  
Particle Mass ◽  
Single Particle Mass Spectrometry ◽  
Tropospheric Aerosol

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

2020 ◽  
Author(s):  
Johannes Passig ◽  
Julian Schade ◽  
Ellen Iva Rosewig ◽  
Robert Irsig ◽  
Thomas Kröger-Badge ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Excimer Laser ◽  
Single Particle ◽  
Aerosol Particles ◽  
Ambient Air ◽  
Particle Mass ◽  
Anthropogenic Aerosols ◽  
Single Particle Mass Spectrometry ◽  
Krf Excimer Laser ◽  
The Impact

Abstract. We describe resonance effects in laser desorption/ionization (LDI) of particles that substantially increase the sensitivity and selectivity to metals in single particle mass spectrometry (SPMS). Within the proposed scenario, resonant light absorption by ablated metal atoms increases their ionization rate within a single laser pulse. By choosing the appropriate laser wavelength, the key micronutrients Fe, Zn and Mn can be detected on individual aerosol particles with considerably improved efficiency. These ionization enhancements for metals apply to natural dust and anthropogenic aerosols, both important sources of bioavailable metals to marine environments. Transferring the results into applications, we show that the spectrum of our KrF-excimer laser is in resonance with a major absorption line of iron atoms. To estimate the impact of resonant LDI on the metal detection efficiency in SPMS applications, we performed a field experiment on ambient air with two alternately firing excimer lasers of different wavelengths. Herein, resonant LDI with the KrF-excimer laser (248.3 nm) revealed Fe signatures for many more aerosol particles compared to the more common ArF-excimer laser line of 193.3 nm. Moreover, resonant ionization of iron appeared to be less dependent on the particle matrix than conventional non-resonant LDI, allowing a more universal and secure detection of Fe. Our findings show a way to improve the detection and source attribution capabilities of SPMS for particle-bound metals, a health-relevant aerosol component and an important source of micronutrients to the surface oceans affecting marine primary productivity.

Depth Profiling of Heterogeneously Mixed Aerosol Particles Using Single-Particle Mass Spectrometry

2002 ◽  
Vol 74 (7) ◽  
pp. 1642-1649 ◽  
Author(s):  
Ephraim Woods ◽  
Geoffrey D. Smith ◽  
Roger E. Miller ◽  
Tomas Baer
Keyword(s):  
Mass Spectrometry ◽  
Single Particle ◽  
Aerosol Particles ◽  
Depth Profiling ◽  
Particle Mass ◽  
Single Particle Mass Spectrometry

Source apportionment of lead-containing aerosol particles in Shanghai using single particle mass spectrometry

Chemosphere ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. 501-507 ◽  
Author(s):  
Yaping Zhang ◽  
Xiaofei Wang ◽  
Hong Chen ◽  
Xin Yang ◽  
Jianmin Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Source Apportionment ◽  
Single Particle ◽  
Aerosol Particles ◽  
Particle Mass ◽  
Single Particle Mass Spectrometry

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

2019 ◽  
Vol 12 (4) ◽  
pp. 2219-2240 ◽  
Author(s):  
Xiaoli Shen ◽  
Harald Saathoff ◽  
Wei Huang ◽  
Claudia Mohr ◽  
Ramakrishna Ramisetty ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sodium Salt ◽  
Single Particle ◽  
Total Mass ◽  
Particle Number ◽  
Aerosol Particles ◽  
Particle Mass ◽  
Particle Identification ◽  
Refractory Compounds ◽  
Single Particle Mass Spectrometry

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.

scholarly journals Understanding of atmospheric aerosol particles with improved particle identification and quantification by single particle mass spectrometry

2018 ◽  
Author(s):  
Xiaoli Shen ◽  
Harald Saathoff ◽  
Wei Huang ◽  
Claudia Mohr ◽  
Ramakrishna Ramisetty ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometer ◽  
Single Particle ◽  
Total Mass ◽  
Aerosol Particles ◽  
Particle Mass ◽  
Fuzzy Classification ◽  
Rural Site ◽  
Refractory Compounds ◽  
Single Particle Mass Spectrometry

Abstract. Single particle mass spectrometry (SPMS) is a useful, albeit not fully quantitative tool to determine chemical composition and mixing state of aerosol particles in the atmosphere. During a six-week field campaign in summer 2016 at a rural site in the upper Rhine valley near Karlsruhe city in southwest Germany, ~3.7 x 105 single particles were analyzed by 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 identification and well characterized overall detection efficiency (ODE) for this instrument, particle similarity can be transferred into corrected number fractions and further transferred into mass fractions. Considering the entire measurement period, Potassium rich and aromatics coated dust (class 5) dominated the particle number (46.5 % number fraction) and mass (36.0 % mass fraction); Sodium salts like particles (class 3) were the second lowest in number (3.5 %), but the second dominating class in terms of particle mass (25.3 %). This difference demonstrates the crucial role of particle mass quantification for SPMS data. Using corrections for maximum, mean, and minimum ODE, the total mass of the quantified particles measured by LAAPTOF accounts for ~12 %, ~25 %, and ~104 % of the total mass measured by an aerosol mass spectrometer (AMS) with a collection efficiency of 0.5. These two mass spectrometers show a good correlation (correlation coefficient γ > 0.6) regarding total mass for more than 70 % of the measurement time, indicating non-refractory species measured by AMS might 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. Furthermore, our approach allows for the first time to assign the non-refractory compounds measured by AMS to different particle classes. Overall AMS-nitrate was mainly arising from class 3, while class 5 was dominant during events rich in organic aerosol particles.

scholarly journals Improved identification of primary biological aerosol particles using single particle mass spectrometry

2016 ◽  
Author(s):  
Maria A. Zawadowicz ◽  
Karl D. Froyd ◽  
Daniel M. Murphy ◽  
Daniel J. Cziczo
Keyword(s):  
Mass Spectrometry ◽  
Single Particle ◽  
Mass Spectra ◽  
Aerosol Particles ◽  
Particle Mass ◽  
Cloud Formation ◽  
Single Particle Mass Spectrometry ◽  
Ambient Data ◽  
By Products ◽  
Biological Aerosol Particles

Abstract. Measurements of primary biological aerosol particles, especially at altitudes relevant to cloud formation, are scarce. Single particle mass spectrometry (SPMS) has been used to probe aerosol chemical composition from ground and aircraft for over 20 years. Here we develop a method for identifying bioaerosols using SPMS. We show that identification of bioaerosol using SPMS is complicated because phosphorus-bearing mineral dust and phosphorus-rich combustion by-products such as fly ash produce mass spectra with peaks similar to those typically used as markers for bioaerosol. We have developed a methodology to differentiate and identify bioaerosol using machine learning statistical techniques applied to mass spectra of known particle types. This improved method provides far fewer false positives compared to approaches reported in the literature. The new method was then applied to ambient data collected at Storm Peak Laboratory to show that 0.04–0.3 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol.

scholarly journals Improved identification of primary biological aerosol particles using single-particle mass spectrometry

2017 ◽  
Vol 17 (11) ◽  
pp. 7193-7212 ◽  
Author(s):  
Maria A. Zawadowicz ◽  
Karl D. Froyd ◽  
Daniel M. Murphy ◽  
Daniel J. Cziczo
Keyword(s):  
Mass Spectrometry ◽  
Single Particle ◽  
Mass Spectra ◽  
Mineral Dust ◽  
Aerosol Particles ◽  
Central Valley ◽  
Particle Mass ◽  
Cloud Formation ◽  
Single Particle Mass Spectrometry ◽  
Biological Aerosol Particles

Abstract. Measurements of primary biological aerosol particles (PBAP), especially at altitudes relevant to cloud formation, are scarce. Single-particle mass spectrometry (SPMS) has been used to probe aerosol chemical composition from ground and aircraft for over 20 years. Here we develop a method for identifying bioaerosols (PBAP and particles containing fragments of PBAP as part of an internal mixture) using SPMS. We show that identification of bioaerosol using SPMS is complicated because phosphorus-bearing mineral dust and phosphorus-rich combustion by-products such as fly ash produce mass spectra with peaks similar to those typically used as markers for bioaerosol. We have developed a methodology to differentiate and identify bioaerosol using machine learning statistical techniques applied to mass spectra of known particle types. This improved method provides far fewer false positives compared to approaches reported in the literature. The new method was then applied to two sets of ambient data collected at Storm Peak Laboratory and a forested site in Central Valley, California to show that 0.04–2 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol. In addition, 36–56 % of particles identified as biological also contained spectral features consistent with mineral dust, suggesting internal dust–biological mixtures.

Sign in / Sign up

Export Citation Format

Share Document