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.

Some Recent Advances in Fluorescence Spectroscopy

“…Bowen et al (2004) examined their effectiveness in the identification of fluorescence events from single molecules Pulsed-source time-resolved fluorescence lifetime methods were enhanced by Dolenko et al (2002b), who showed that ANNs could help to resolve noninteracting twocomponent dye mixtures in cases where the lifetimes were shorter than both the exciting light pulse and the detector gate time. Overall it is clear that ANN methods have a great deal to offer to the variety of data-intensive fluorescence techniques now available to researchers, and this area will surely increase in importance, especially as ANN software is now readily available.…”

Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement

“…Compared to choosing detection hardware, automatic target segmentation is a more challenging task in image understanding and computer vision due to the variety, low contrast, and uneven illumination of chemical spill images. Many methods of image segmentation have been proposed to detect targets on water surfaces, including thresholding [17], level sets [18], active contour models [19], the Mean-Shift [20], and neural networks [21]. The level sets method is a branch of active contour model, which represents contour as the zero level set of a higher dimensional function.…”

Modification of optical properties of seawater exposed to oil contaminants based on excitation-emission spectra

“…Petroleum substances absorb light in the short wavelengths of the visual part of the spectrum (VIS) [2]- [4] and if they are excited by ultraviolet light (UV) they emit light in UV and VIS [5]- [7]. However, if an in-water or above-water optical detector of petroleum substances (oil) is employed, integrated information on the spectroscopic features of an oil and the natural constituents of the water should be known.…”