Articles | Volume 8, issue 10
https://doi.org/10.5194/amt-8-4013-2015
https://doi.org/10.5194/amt-8-4013-2015
Research article
 | 
02 Oct 2015
Research article |  | 02 Oct 2015

Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental carbon

A. M. Dillner and S. Takahama

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Andreae, M. O. and Gelencsér, A.: Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols, Atmos. Chem. Phys., 6, 3131–3148, https://doi.org/10.5194/acp-6-3131-2006, 2006.
Birch, M. E. and Cary, R. A.: Elemental carbon-based method for occupational monitoring of particulate diesel exhaust: Methodology and exposure issues, Analyst, 121, 1183–1190, https://doi.org/10.1039/an9962101183, 1996.
Bishop, C.: Pattern recognition and machine learning, Springer-Verlag, New York, 2006.
Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles: An investigative review, Aerosol Sci. Tech., 40, 27–67, https://doi.org/10.1080/02786820500421521, 2006.
Bond, T. C., Anderson, T. L., and Campbell, D.: Calibration and intercomparison of filter-based measurements of visible light absorption by aerosols, Aerosol Sci. Tech., 30, 582–600, https://doi.org/10.1080/027868299304435, 1999.
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Short summary
Elemental carbon (EC), a constituent of atmospheric particulate matter (PM), adversely affects climate, visibility and human health. EC is measured in PM monitoring networks world-wide but the method is expensive and destructive to the samples. Here, methods are presented to accurately predict EC using Fourier transform infrared (FT-IR) analysis which is inexpensive and non-destructive. This method complements measurements of organic carbon and organic functional groups made using FT-IR.