Articles | Volume 16, issue 12
https://doi.org/10.5194/amt-16-3273-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3273-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2023
Research article |
| 29 Jun 2023
| 29 Jun 2023
Generalized Kendrick analysis for improved visualization of atmospheric mass spectral data
Mitchell W. Alton
Department of Chemistry and Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
80309, USA
now at: Aerodyne Research Inc., Billerica, Massachusetts 01821, USA
Harald J. Stark
Department of Chemistry and Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
80309, USA
Aerodyne Research Inc., Billerica, Massachusetts 01821, USA
Manjula R. Canagaratna
Aerodyne Research Inc., Billerica, Massachusetts 01821, USA
Eleanor C. Browne
CORRESPONDING AUTHOR
Department of Chemistry and Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
80309, USA
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Organic aerosols influence global climate though uncertainties in the magnitude of this influence remain large. A comprehensive understanding of the molecular composition of aerosols is key to minimizing these uncertainties. We present a new method for analyzing organic aerosols in real time using ion mobility mass spectrometry, enabling the separation of aerosol components with identical mass. Analysis of standards and laboratory-generated aerosols demonstrate the method’s potential.
Andre F. Schaum, Kelvin H. Bates, Kyung-Eun Min, Faith Myers, Emmaline R. Longnecker, Manjula R. Canagaratna, Mitchell W. Alton, and Paul J. Ziemann
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Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
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Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.
Mahen Konwar, Benjamin Werden, Edward C. Fortner, Sudarsan Bera, Mercy Varghese, Subharthi Chowdhuri, Kurt Hibert, Philip Croteau, John Jayne, Manjula Canagaratna, Neelam Malap, Sandeep Jayakumar, Shivsai A. Dixit, Palani Murugavel, Duncan Axisa, Darrel Baumgardner, Peter F. DeCarlo, Doug R. Worsnop, and Thara Prabhakaran
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Jian Zhao, Ella Häkkinen, Frans Graeffe, Jordan E. Krechmer, Manjula R. Canagaratna, Douglas R. Worsnop, Juha Kangasluoma, and Mikael Ehn
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Atmos. Meas. Tech., 14, 6955–6972, https://doi.org/10.5194/amt-14-6955-2021, https://doi.org/10.5194/amt-14-6955-2021, 2021
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To understand the sources and fate of particulate matter in the atmosphere, the ability to quantitatively describe its chemical composition is essential. In this work, we developed a calibration method for a state-of-the-art measurement technique without the need for chemical standards. Statistical analyses identified the driving factors behind instrument sensitivity variability towards individual components of particulate matter.
Chenyang Bi, Jordan E. Krechmer, Graham O. Frazier, Wen Xu, Andrew T. Lambe, Megan S. Claflin, Brian M. Lerner, John T. Jayne, Douglas R. Worsnop, Manjula R. Canagaratna, and Gabriel Isaacman-VanWertz
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Iodide-adduct chemical ionization mass spectrometry (I-CIMS) has been widely used to analyze airborne organics. In this study, I-CIMS sensitivities of isomers within a formula are found to generally vary by 1 and up to 2 orders of magnitude. Comparisons between measured and predicted moles, obtained using a voltage-scanning calibration approach, show that predictions for individual compounds or formulas might carry high uncertainty, yet the summed moles of analytes agree reasonably well.
Chenyang Bi, Jordan E. Krechmer, Manjula R. Canagaratna, and Gabriel Isaacman-VanWertz
Atmos. Meas. Tech., 14, 6551–6560, https://doi.org/10.5194/amt-14-6551-2021, https://doi.org/10.5194/amt-14-6551-2021, 2021
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Calibration techniques have been recently developed to log-linearly correlate analyte sensitivity with CIMS operating conditions particularly for compounds without authentic standards. In this work, we examine the previously ignored bias in the log-linear-based calibration method and estimate an average bias of 30 %, with 1 order of magnitude for less sensitive compounds in some circumstances. A step-by-step guide was provided to reduce and even remove the bias.
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Short summary
Mass spectrometric measurements of atmospheric composition routinely detect hundreds of different ions of varying chemical composition, creating challenges for visualization and data interpretation. We present a new analysis technique to facilitate visualization, while providing greater chemical insight. Additionally, it can aid in identifying the chemical composition of ions. A graphical user interface for performing the analysis is introduced and freely available, enabling broad applications.
Mass spectrometric measurements of atmospheric composition routinely detect hundreds of...
