Ambient aerosol composition by infrared spectroscopy and partial least-squares in the chemical speciation network: Organic carbon with functional group identification

ABSTRACT

The Fourier-transform infrared (FT-IR) spectra of ambient fine aerosols were used with partial least-squares (PLS) regression to accurately, inexpensively, and nondestructively predict organic carbon (OC) on polytetrafluoroethylene (PTFE) filters in the U.S. Environmental Protection Agencies' Chemical Speciation Network (CSN). Recently, a similar FT-IR method was used for OC determination in the rural United States Interagency Monitoring of PROtected Visual Environments network, with the present work extending the method to urban aerosols with low mass loadings. In the present study, FT-IR spectra were calibrated to collocated thermal/optical reflectance (TOR) OC measurements following numerical processing with a second derivative filter, backward Monte Carlo unimportant variable elimination, and a quadratic discriminant analysis-PLS vapor correction routine. After processing and vapor correcting spectra, the number of model components (latent variables) were reduced from thirty-five to three with only the first PLS component patently predicting OC. The two lesser components modeled PTFE and inorganic interference remaining in the spectra. A wavenumber ranking procedure— using both the variable importance in projection and bootstrapped confidence intervals— underscored the primacy of aliphatic C-H stretches and carbonyl vibrations for OC prediction. Aliphatic deformations, amines, organonitrate, carboxylate, and aromatic vibrations were also valuable for OC quantification. This study demonstrates that PLS models quantifying TOR OC are explicable in terms of organic functional group absorption after judiciously processing FT-IR spectra.

Copyright © 2016 American Association for Aerosol Research

EDITOR:

• Matti Maricq

Acknowledgments

Thanks to S.M. Raffuse for the map of the CSN sites and to the RTI International team for managing CSN.

Funding

The authors acknowledge funding from U.S. EPA and the IMPROVE program (National Park Service cooperative agreement P11AC91045) and EPFL funding.