OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 41, Iss. 24 — Aug. 20, 2002
  • pp: 5155–5166

Fluorescence Diagnostics of Oil Pollution in Coastal Marine Waters by use of Artificial Neural Networks

Tatiana A. Dolenko, Victor V. Fadeev, Irina V. Gerdova, Serge A. Dolenko, and Rainer Reuter  »View Author Affiliations


Applied Optics, Vol. 41, Issue 24, pp. 5155-5166 (2002)
http://dx.doi.org/10.1364/AO.41.005155


View Full Text Article

Acrobat PDF (191 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We discuss the problems with and the real possibilities of determining oil pollution <i>in situ</i> in coastal marine waters with fluorescence spectroscopy and of using artificial neural networks for data interpretation. In general, the fluorescence bands of oil and aquatic humic substance overlap. At oil concentrations in water from a few to tens of micrograms per liter, the intensity of oil fluorescence is considerably lower than that of humic substances at concentrations that typically are present in coastal waters. Therefore it is necessary to solve the problem of separating the small amount of oil fluorescence from the humic substance background in the spectrum. The problem is complicated because of possible interactions between the components and variations in the parameters of the fluorescence bands of humic substances and oil in water. Fluorescence spectra of seawater samples taken from coastal areas of the Black Sea, samples prepared in the laboratory, and numerically simulated spectra were processed with an artificial neural network. The results demonstrate the possibility of estimating oil concentrations with an accuracy of a few micrograms per liter in coastal waters also in cases in which the contribution from other organic compounds, primarily humic substances, to the fluorescence spectrum exceeds that of oil by 2 orders of magnitude and more.

© 2002 Optical Society of America

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(100.3190) Image processing : Inverse problems
(300.2530) Spectroscopy : Fluorescence, laser-induced
(300.6280) Spectroscopy : Spectroscopy, fluorescence and luminescence

Citation
Tatiana A. Dolenko, Victor V. Fadeev, Irina V. Gerdova, Serge A. Dolenko, and Rainer Reuter, "Fluorescence Diagnostics of Oil Pollution in Coastal Marine Waters by use of Artificial Neural Networks," Appl. Opt. 41, 5155-5166 (2002)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-41-24-5155


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. R. P. Lippman, “An introduction to computing with neural nets,” IEEE Trans. Acoust. Speech Signal Process. 4(2), 4–22 (1987).
  2. D. Specht, “A general regression neural network,” IEEE Trans. Neural Netw. 2, 568–589 (1991).
  3. J. Lakovich, Principles of Fluorescence Spectroscopy (Plenum, New York, 1986).
  4. A. W. Hornig, “Identification, estimation and monitoring of petroleum in marine waters by luminescence methods,” in Marine Pollution Monitoring, NBS Spec. Publ. 409, 135–144 (1974).
  5. R. M. Measures, Laser Remote Sensing. Fundamentals and Applications (Wiley, New York, 1984).
  6. R. A. Velapoldi and K. D. Mielenz, A Fluorescence Standard Reference Material: Quinine Sulfate Dihydrate, NBS Spec. Publ. SP-260 64 (National Bureau of Standards, Washington, DC, 1980).
  7. D. V. Maslov, V. V. Fadeev, and A. I. Lyashenko, “A shore-based lidar for coastal seawater monitoring,” in European Association of Remote Sensing Laboratories (EARSeL) eProceedings (Workshop on LIDAR on Land and Sea), R. Reuter, ed. (EARSeL, Paris, 2001), pp. 46–52.
  8. E. M. Filippova, V. V. Chubarov, and V. V. Fadeev, “New possibilities of laser fluorescence spectroscopy for diagnostics of petroleum hydrocarbons in natural water,” Can. J. Appl. Spectrosc. 38, 139–144 (1993).
  9. V. V. Fadeev, T. A. Dolenko, E. M. Filippova, and V. V. Chubarov, “Saturation spectroscopy as a method for determining the photophysical parameters of complicated organic compounds,” Opt. Commun. 166, 25–33 (1999).
  10. S. Determann, R. Reuter, P. Wagner, and R. Willkomm, “Fluorescent matter in the eastern Atlantic Ocean. 1. Method of measurement and near-surface distribution,” Deep-Sea Res. I 41, 659–675 (1994).
  11. S. Determann, R. Reuter, and R. Willkomm, “Fluorescent matter in the eastern Atlantic Ocean. 2. Vertical profiles, and relation to water masses,” Deep-Sea Res. I 43, 345–360 (1996).
  12. B. Nieke, R. Reuter, R. Heuermann, H. Wang, M. Babin, and J. C. Therriault, “Light absorption and fluorescence properties of chromophoric dissolved organic matter (CDOM) in the St. Lawrence Estuary (Case 2 waters),” Cont. Shelf Res. 17, 235–252 (1997).
  13. D. N. Klyshko and V. V. Fadeev, “Remote determination of the admixture concentrations in water the method of laser spectroscopy using Raman scattering as an internal standard,” Sov. Phys. Dokl. 23, 55–57 (1978).
  14. V. V. Fadeev, “Possibilities of standardization of normalized fluorescent parameter as a measure of organic admixtures concentration in water and atmosphere,” in Environmental Sensing and Applications, M. Carleer, M. Hilton, T. Lamp, R. Reuter, G. M. Russwurm, K. Schafer, K. Weber, K. Weitkamp, J.-P. Wolf, and L. Woppowa, eds., Proc. SPIE 3821, 458–466 (1999).
  15. Intergovernmental Oceanographic Comission/United Nations Environmental Programme, Manual for Monitoring Oil and Dissolved/Dispersed Petroleum Hydrocarbons in Marine Waters and on Beaches, Manuals and Guides No. 13 UN Educational, Scientific, and Cultural Organization, Paris, 1984).
  16. P. Coble, S. A. Green, N. V. Blough, and R. B. Gagosian, “Characterization of dissolved organic matter in the Black Sea by fluorescence spectroscopy,” Nature 348, 432–435 (1990).
  17. R. F. Chen and J. L. Bada, “The fluorescence of dissolved organic matter in seawater,” Mar. Chem. 37, 191–221 (1992).
  18. K. Mopper and C. A. Schultz, “Fluorescence as a possible tool for studying the nature and water column distribution of DOC components,” Mar. Chem. 41, 229–238 (1993).
  19. M. M. De Souza Sierra, O. F. X. Donard, M. Lamotte, C. Belin, and M. Ewald, “Fluorescence spectroscopy of coastal and marine waters,” Mar. Chem. 47, 127–144 (1994).
  20. P. G. Coble, “Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy,” Mar. Chem. 51, 325–346 (1996).
  21. S. Determann, J. Lobbes, R. Reuter, and J. Rullkötter, “UV fluorescence excitation and emission spectroscopy of marine algae and bacteria,” Mar. Chem. 62, 137–156 (1998).
  22. A. I. Simonov and V. I. Mikhailov, “Chemical pollution of thin surface layer of the World Ocean,” Tr. Gos. Okeanogr. Inst. 149, 5–16 (1979).
  23. S. Babichenko, L. Poryvkina, and S. Kaitala, “Multiple-wavelength remote sensing of phytoplankton,” EARSeL Adv. Remote Sens. 3, 78–83 (1995).
  24. K. Hennig, T. de Vries, R. Paetzold, K. Jantos, E. Voss, and A. Anders, “Multi sensor system for fast analyses in environmental monitoring with application in waste water treatment,” in European Association of Remote Sensing Laboratories (EARSeL) eProceedings, R. Reuter, ed. (EARSeL, Paris, 2001), Vol. 1, pp. 61–67.
  25. S. A. Dolenko, T. A. Dolenko, V. V. Fadeev, E. M. Filippova, O. V. Kozyreva, and I. G. Persiantsev, “Solution of inverse problem in nonlinear laser fluorimetry of organic compounds with the use of artificial neural networks,” Pattern Recognition Image Anal. 9, 510–515 (1999).
  26. E. M. Filippova, V. V. Fadeev, and V. V. Chubarov, “The origin and structure of fluorescence band from aquatic humic substances,” in 5th International Conference on Laser Application in Life Sciences, P. A. Apanasevich, N. I. Koroteev, S. G. Kruglik, and V. N. Zadkov, eds., Proc. SPIE 2370, 651–655 (1994).
  27. A. G. Abroskin, S. E. Nol’de, V. V. Fadeev, and V. V. Chubarov, “Laser fluorimetry determination of emulsified-dissolved oil in water,” Sov. Phys. Dokl. 33, 215–217 (1988).
  28. T. Hengstermann and R. Reuter, “Laser remote sensing of pollution of the sea: a quantitative approach,” EARSeL Adv. Remote Sens. 1, 52–60 (1992).
  29. A. N. Tikhonov and V. Ya. Arsenin, Methods of Solving Ill-Posed Problems, Scripta Series in Mathematics (Scripta Mathematics, New York, 1977).
  30. I. V. Boychuk, T. A. Dolenko, A. R. Sabirov, V. V. Fadeev, and E. M. Filippova, “Study of the uniqueness and stability of the solution of inverse problem in saturation fluorimetry,” Quantum Electron. 30, 611–616 (2000).
  31. H. R. Madala and A. G. Ivakhnenko, Inductive Learning Algorithms for Complex Systems Modeling (CRC Press, Boca Raton, Fla., 1994).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited