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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 11 Nov 2019

Submitted as: research article | 11 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

Charlotte Bürki1, Matteo Reggente1, Ann M. Dillner2, Jenny L. Hand3, Stephanie L. Shaw4, and Satoshi Takahama1 Charlotte Bürki et al.
  • 1ENAC/IIE Swiss Federal Institute of Technology Lausanne (EFPL), Lausanne, CH-1015, Switzerland
  • 2Air Quality Research Center, University of California Davis, Davis, CA 95616, USA
  • 3Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA
  • 4Electric Power Research Institute, Palo Alto, CA, 94304, United States

Abstract. The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM2.5) contain many important absorption bands relevant for characterizing organic matter (OM) and obtaining organic matter to organic carbon (OM/OC) ratios. However, extracting this information quantitatively – accounting for overlapping absorption bands and relating absorption to molar abundance – poses several challenges. For instance, a subset of model parameters lead to calibrations that test almost indistinguishably well against laboratory standards generate substantially different predictions in ambient samples. Furthermore, additional parameters related to molecular structure are required to estimate carbon content from functional group (FG) abundance. However, since many carbon atoms can be branched (not fully functionalized) or polyfunctional, these parameters are not well constrained for ambient sample mixtures.

In this work, we present a probabilistic framework to characterize combinations of these parameters that are consistent with field measurements of organic carbon (OC), for which estimates from thermal optical reflectance (TOR) measurements are used. Uncertainties in this probabilistic framework characterize the plausibility of many different parameter values that yield acceptable predictions (to the extent that they can be evaluated) neglected in conventional estimates of statistical uncertainties. Based on calibrations of aliphatic CH, alcohol COH, carboxylic acid COO, carboxylate COO, and amine NH2, we find model parameters for approximately homogeneous groups of samples determined from cluster analysis of FTIR spectra available for 17 sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network (7 sites in 2011 and 10 additional sites in 2013). These groups are interpreted as being predominantly influenced by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols.

The resulting calibrations reproduce TOR OC concentrations (R2 = 0.96) and provide OM/OC values consistent with our current best estimate of ambient OC. The mean OM/OC ratios corresponding to sample types determined from cluster analysis range between 1.4 and 2.0, though ratios for individual samples exhibit a larger range. Trends in OM/OC for sites aggregated by region or year are compared with another regression approach for estimating OM/OC ratios from a mass balance of the major chemical species contributing to PM fine mass. Differences in OM/OC estimates are observed according to estimation method and are explained through the sample types determined from spectral profiles of the PM.

Charlotte Bürki et al.
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Charlotte Bürki et al.
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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.
Infrared spectroscopy is a chemically informative method for particulate matter...