10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Bounding Uncertainty in Functional Group Reconstruction of Organic Carbon and Organic Matter Concentrations in PM2.5 For the Improve Monitoring Network
MATTEO REGGENTE, Ann Dillner, Satoshi Takahama, EPFL
Abstract Number: 1305 Working Group: Instrumentation
Abstract Functional group analysis by Fourier Transform Infrared (FT-IR) spectroscopy provides a chemically informative representation of the organic matter (OM) mass in aerosols. The molecular mixture conceptualized as a collection of functionalized carbon atoms can be used to estimate organic carbon (OC) content, degree of oxygenation, oxidation state, and organic matter to organic carbon mass (OM/OC) ratios for a large portion of the aerosol organic fraction (Takahama and Ruggeri, 2017). In previous work, this technique has been used to provide complementary information to mass spectrometry measurements which provide molecular speciation for a small fraction of the mass, or mass fragment composition commonly used for OM characterization (e.g., Faber 2017).
Functional group calibrations are typically prepared in the laboratory using simple mixtures and extrapolated to ambient aerosol spectra which contain a diverse set of compounds. While the potential of FTIR for quantitative aerosol research using this approach has been studied in separate contexts, a thorough investigation into the consistency of functional group estimation has not yet been conducted. In this work, we systematically explore the influence of spectral processing (e.g., baseline correction), selection of laboratory standards, and calibration algorithms on predictions of functional group distributions. Using 250 laboratory standards and 794 ambient sample spectra from 7 urban and rural sites in the IMPROVE monitoring network, we present the range of predictions that are possible under different assumptions. We also compare the physically-based peak-parameter representation for building calibration models to statistically-based multivariate regression models and their contrasting strengths, particularly for constraining the carbonyl apportionment to carboxyl and other non-acid functional groups. We also identify samples with higher loadings of larger particles from mineral dust and other sources that lead to excessive scattering and the Christiansen peak effect, which affect the quality of predictions. Directions for future research resulting from this analysis are presented. [1] Faber, P., Drewnick, F., Bierl, R., Borrmann, S., 2017. Complementary online aerosol mass spectrometry and offline FT-IR spectroscopy measurements: Prospects and challenges for the analysis of anthropogenic aerosol particle emissions. Atmospheric Environment 166, 92–98. https://doi.org/10.1016/j.atmosenv.2017.07.014. [2] Russell, L.M., Bahadur, R., Hawkins, L.N., Allan, J., Baumgardner, D., Quinn, P.K., Bates, T.S., 2009. Organic aerosol characterization by complementary measurements of chemical bonds and molecular fragments. Atmospheric Environment 43, 6100–6105. https://doi.org/10.1016/j.atmosenv.2009.09.036. [3] Takahama, S., Ruggeri, G., 2017. Technical note: Relating functional group measurements to carbon types for improved model–measurement comparisons of organic aerosol composition. Atmos. Chem. Phys. 17, 4433–4450. https://doi.org/10.5194/acp-17-4433-2017. [4] Takahama, S., Johnson, A., Russell, L.M., 2013. Quantification of Carboxylic and Carbonyl Functional Groups in Organic Aerosol Infrared Absorbance Spectra. Aerosol Science and Technology 47, 310–325. https://doi.org/10.1080/02786826.2012.752065. [5] Ruthenburg, T.C., Perlin, P.C., Liu, V., McDade, C.E., Dillner, A.M., 2014. Determination of organic matter and organic matter to organic carbon ratios by infrared spectroscopy with application to selected sites in the IMPROVE network. Atmospheric Environment 86, 47–57. https://doi.org/10.1016/j.atmosenv.2013.12.034.