Fluorescence diagnostics of oil pollution in coastal marine waters by use of artificial neural networks.

Abstract

We discuss the problems with and the real possibilities of determining oil pollution in situ 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.