Applied Physics B

, Volume 79, Issue 4, pp 525–530

Multi-component chemical analysis of gas mixtures using a continuously tuneable lidar system

  • P. Weibring
  • C. Abrahamsson
  • M. Sjöholm
  • J.N. Smith
  • H. Edner
  • S. Svanberg
Article

Abstract

Differential absorption lidar (DIAL) measurements are usually made on single compounds by alternately switching the wavelength between on and off a resonance line. The selection of more than two wavelengths is a mathematical necessity for simultaneous measurement of multiple species or for resolving interference effects between a compound of interest and a background gas such as water vapour or carbon dioxide. This is especially true in the mid-IR region, where many hydrocarbon compounds have important spectral features. We present a method for remote measurement of gas mixtures in the mid-IR region based on a newly developed fast-switching, frequency-agile optical parametric oscillator lidar transmitter. A multivariate statistical procedure has also been applied for this system, which combines a genetic algorithm for wavelength selection with a partial least squares method for identifying individual compounds from their combined absorption spectrum. A calibration transfer is performed for compounds of interest using reference spectra from an absorption spectra database. Both indoor absorption cell measurements and outdoor remote range resolved measurements of hydrocarbon mixtures were performed to explore the performance of the method.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.H. Seinfeld, S.N. Pandi: Atmospheric Chemistry and Physics (John Wiley & Sons, New York 1988)Google Scholar
  2. 2.
    M.W. Sigrist: Air monitoring by spectroscopic techniques, In: Chemical Analysis, vol. 127 (John Wiley & Sons, New York 1994)Google Scholar
  3. 3.
    J. Kasparian, M. Rodriguez, G. Méjean, J. Yu, E. Salmon, H. Wille, R. Bourayou, S. Frey, Y.-B. André, A. Mysyrowicz, R. Sauerbrey, J.-P. Wolf , L. Wöste: Science 301, 61 (2003)ADSCrossRefGoogle Scholar
  4. 4.
    R.A. Robinson, P.T. Woods, M.J.T. Milton: SPIE 2506, 140 (1995)ADSGoogle Scholar
  5. 5.
    J.R. Quagliano, P.O. Stoutland, R.R. Petrin, R.K. Sander, R.J. Romero, M.C. Whitehead, C.R. Quick, J.J. Tiee, L.J. Jolin: SPIE 2702, 16 (1996)ADSGoogle Scholar
  6. 6.
    P. Weibring, J. Smith, H. Edner, S. Svanberg: Rev. Sci. Instrum. 74, 4478 (2003)ADSCrossRefGoogle Scholar
  7. 7.
    P. Geladi, B.R. Kowalski: Anal. Chim. Acta 185, 1 (1986)CrossRefGoogle Scholar
  8. 8.
    S.J. Haswell, A.D. Walmsley: Anal. Chim. Acta 400, 399 (1999)CrossRefGoogle Scholar
  9. 9.
    R. Leardi, R. Boggia, M. Terrile: J. Chemom. 6, 267 (1992)CrossRefGoogle Scholar
  10. 10.
    A.S. Bangalore, R.E. Shaffer, G.W. Small, M.A. Arnold: Anal. Chem. 68, 4200 (1996)CrossRefGoogle Scholar
  11. 11.
    L. Davies: Handbook of Genetic Algorithms (Van Nostrand Reinhold, New York 1991)Google Scholar
  12. 12.
    P. Weibring, H. Edner, S. Svanberg: Appl. Opt. 42, 1 (2003)CrossRefGoogle Scholar
  13. 13.
    T. Fujii, T. Fukuchi, N. Goto, K. Nemoto, N. Takeuchi: App. Opt. 40, 949 (2001)ADSCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • P. Weibring
    • 1
  • C. Abrahamsson
    • 1
  • M. Sjöholm
    • 1
  • J.N. Smith
    • 1
  • H. Edner
    • 1
  • S. Svanberg
    • 1
  1. 1.Department of PhysicsLund Institute of TechnologyLundSweden

Personalised recommendations