Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-2995-2020
https://doi.org/10.5194/amt-13-2995-2020
Research article
 | 
09 Jun 2020
Research article |  | 09 Jun 2020

Comparison of dimension reduction techniques in the analysis of mass spectrometry data

Sini Isokääntä, Eetu Kari, Angela Buchholz, Liqing Hao, Siegfried Schobesberger, Annele Virtanen, and Santtu Mikkonen

Related authors

Hygroscopicity and CCN potential of DMS-derived aerosol particles
Bernadette Rosati, Sini Isokääntä, Sigurd Christiansen, Mads Mørk Jensen, Shamjad P. Moosakutty, Robin Wollesen de Jonge, Andreas Massling, Marianne Glasius, Jonas Elm, Annele Virtanen, and Merete Bilde
Atmos. Chem. Phys., 22, 13449–13466, https://doi.org/10.5194/acp-22-13449-2022,https://doi.org/10.5194/acp-22-13449-2022, 2022
Short summary
The effect of clouds and precipitation on the aerosol concentrations and composition in a boreal forest environment
Sini Isokääntä, Paul Kim, Santtu Mikkonen, Thomas Kühn, Harri Kokkola, Taina Yli-Juuti, Liine Heikkinen, Krista Luoma, Tuukka Petäjä, Zak Kipling, Daniel Partridge, and Annele Virtanen
Atmos. Chem. Phys., 22, 11823–11843, https://doi.org/10.5194/acp-22-11823-2022,https://doi.org/10.5194/acp-22-11823-2022, 2022
Short summary
Atmospheric aging of small-scale wood combustion emissions (model MECHA 1.0) – is it possible to distinguish causal effects from non-causal associations?
Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-13,https://doi.org/10.5194/gmd-2020-13, 2020
Revised manuscript not accepted
Technical note: Effects of uncertainties and number of data points on line fitting – a case study on new particle formation
Santtu Mikkonen, Mikko R. A. Pitkänen, Tuomo Nieminen, Antti Lipponen, Sini Isokääntä, Antti Arola, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 19, 12531–12543, https://doi.org/10.5194/acp-19-12531-2019,https://doi.org/10.5194/acp-19-12531-2019, 2019
Short summary

Related subject area

Subject: Aerosols | Technique: Laboratory Measurement | Topic: Data Processing and Information Retrieval
Estimating errors in vehicle secondary aerosol production factors due to oxidation flow reactor response time
Pauli Simonen, Miikka Dal Maso, Pinja Prauda, Anniina Hoilijoki, Anette Karppinen, Pekka Matilainen, Panu Karjalainen, and Jorma Keskinen
Atmos. Meas. Tech., 17, 3219–3236, https://doi.org/10.5194/amt-17-3219-2024,https://doi.org/10.5194/amt-17-3219-2024, 2024
Short summary
Quantifying functional group compositions of household fuel-burning emissions
Emily Y. Li, Amir Yazdani, Ann M. Dillner, Guofeng Shen, Wyatt M. Champion, James J. Jetter, William T. Preston, Lynn M. Russell, Michael D. Hays, and Satoshi Takahama
Atmos. Meas. Tech., 17, 2401–2413, https://doi.org/10.5194/amt-17-2401-2024,https://doi.org/10.5194/amt-17-2401-2024, 2024
Short summary
A new software toolkit for optical apportionment of carbonaceous aerosol
Tommaso Isolabella, Vera Bernardoni, Alessandro Bigi, Marco Brunoldi, Federico Mazzei, Franco Parodi, Paolo Prati, Virginia Vernocchi, and Dario Massabò
Atmos. Meas. Tech., 17, 1363–1373, https://doi.org/10.5194/amt-17-1363-2024,https://doi.org/10.5194/amt-17-1363-2024, 2024
Short summary
Theoretical derivation of aerosol lidar ratio using Mie theory for CALIOP-CALIPSO and OPAC aerosol models
Radhika A. Chipade and Mehul R. Pandya
Atmos. Meas. Tech., 16, 5443–5459, https://doi.org/10.5194/amt-16-5443-2023,https://doi.org/10.5194/amt-16-5443-2023, 2023
Short summary
An extraction method for nitrogen isotope measurement of ammonium in a low-concentration environment
Alexis Lamothe, Joel Savarino, Patrick Ginot, Lison Soussaintjean, Elsa Gautier, Pete D. Akers, Nicolas Caillon, and Joseph Erbland
Atmos. Meas. Tech., 16, 4015–4030, https://doi.org/10.5194/amt-16-4015-2023,https://doi.org/10.5194/amt-16-4015-2023, 2023
Short summary

Cited articles

Äijälä, M., Heikkinen, L., Frohlich, R., Canonaco, F., Prevot, A. S. H., Junninen, H., Petaja, T., Kulmala, M., Worsnop, D., and Ehn, M.: Resolving anthropogenic aerosol pollution types – deconvolution and exploratory classification of pollution events, Atmos. Chem. Phys., 17, 3165–3197, https://doi.org/10.5194/acp-17-3165-2017, 2017. 
Allan, J. D., Jimenez, J. L., Williams, P. I., Alfarra, M. R., Bower, K. N., Jayne, J. T., Coe, H., and Worsnop, D. R.: Quantitative sampling using an Aerodyne aerosol mass spectrometer: 1. Techniques of data interpretation and error analysis, J. Geophys. Res.-Atmos., 108, 4090, doi:10.1029/2002JD002358, 2003. 
Brunet, J. P., Tamayo, P., Golub, T. R., and Mesirov, J. P.: Metagenes and molecular pattern discovery using matrix factorization, P. Natl. Acad. Sci. USA, 101, 4164–4169, https://doi.org/10.1073/pnas.0308531101, 2004. 
Cattel, R. B.: The scree test for the number of factors. Multivariate behavioral research, Multivar. Behav. Res., 1, 245–276, 1966. 
Chakraborty, A., Bhattu, D., Gupta, T., Tripathi, S. N., and Canagaratna, M. R.: Real-time measurements of ambient aerosols in a polluted Indian city: Sources, characteristics, and processing of organic aerosols during foggy and nonfoggy periods, J. Geophys. Res.-Atmos., 120, 9006–9019, https://doi.org/10.1002/2015JD023419, 2015. 
Download
Short summary
Online mass spectrometry produces large amounts of data. These data can be interpreted with statistical methods, enabling scientists to more easily understand the underlying processes. We compared these techniques on car exhaust measurements. We show differences and similarities between the methods and give recommendations on applicability of the methods on certain types of data. We show that applying multiple methods leads to more robust results, thus increasing reliability of the findings.