Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1517-2020
https://doi.org/10.5194/amt-13-1517-2020
Research article
 | 
31 Mar 2020
Research article |  | 31 Mar 2020

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: method development for probabilistic modeling of organic carbon and organic matter concentrations

Charlotte Bürki, Matteo Reggente, Ann M. Dillner, Jenny L. Hand, Stephanie L. Shaw, and Satoshi Takahama

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Cited articles

Adamson, A. W.: A Textbook of Physical Chemistry, Academic Press, 2nd edn., 1979. a
Aiken, A. C., Decarlo, P. F., Kroll, J. H., Worsnop, D. R., Huffman, J. A., Docherty, K. S., Ulbrich, I. M., Mohr, C., Kimmel, J. R., Sueper, D., Sun, Y., Zhang, Q., Trimborn, A., Northway, M., Ziemann, P. J., Canagaratna, M. R., Onasch, T. B., Alfarra, M. R., Prevot, A. S. H., Dommen, J., Duplissy, J., Metzger, A., Baltensperger, U., and Jimenez, J. L.: O/C and OM∕OC ratios of primary, secondary, and ambient organic aerosols with high-resolution time-of-flight aerosol mass spectrometry, Environ. Sci. Technol., 42, 4478–4485, https://doi.org/10.1021/es703009q, 2008. a
Allen, D. T., Palen, E. J., Haimov, M. I., Hering, S. V., and Young, J. R.: Fourier-transform Infrared-spectroscopy of Aerosol Collected In A Low-pressure Impactor (LPI/FTIR) – Method Development and Field Calibration, Aerosol Sci. Tech., 21, 325–342, https://doi.org/10.1080/02786829408959719, 1994. a, b, c
Anderson, J. A. and Seyfried, W. D.: Determination of Oxygenated and Olefin Compound Types by Infrared Spectroscopy, Anal. Chem., 20, 998–1006, https://doi.org/10.1021/ac60023a002, 1948. a
Aster, R. C., Borchers, B., and Thurber, C. H.: Parameter estimation and inverse problems, Academic Press, Waltham, MA, https://doi.org/10.1016/C2009-0-61134-X, 2013. a
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Short summary
Infrared spectroscopy is a chemically informative method for particulate matter characterization. However, recent work has demonstrated that predictions depend heavily on the choice of calibration model parameters. We propose a means for managing parameter uncertainties by combining available data from laboratory standards, molecular databases, and collocated ambient measurements to provide useful characterization of atmospheric organic matter on a large scale.