Το work with title Development of methodologies for the characterization and identification of petroleum contaminants in the environment by Mastrosavvaki Eirini is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
Eirini Mastrosavvaki, "Development of methodologies for the characterization and identification of petroleum contaminants in the environment", Diploma Work, School of Mineral Resources Engineering, Technical University of Crete, Chania, Greece, 2025
https://doi.org/10.26233/heallink.tuc.102375
Petroleum spills to the environment are one of the most important types of pollution of the ecosystem, affecting soil, water resources and the atmosphere. Through the oil pollutants released during the spill, and especially polycyclic aromatic hydrocarbons (PAHs), they can also cause major public health problems due to their toxicity. Identifying and addressing the problem requires a combination of analytical physicochemical methods, which fall under the chemical fingerprinting methodology.The aim of this thesis was to develop methodologies for the characterization and identification of petroleum contaminants from unknown samples from monitoring wells in a refinery environment. In particular, the analytical techniques of gas chromatography-mass spectrometry GC-MS and fluorescence spectroscopy were developed on 14 clean samples of oil fractions and 10 unknown samples from the wells. This identification takes place by exploiting the chemical composition characteristics of the samples, focusing mainly on the fluorescence of polycyclic aromatic hydrocarbons (PAHs).The oil phase of each sample was first subjected to gas chromatography-mass spectroscopy analysis to extract information on the chemical composition and distribution of aromatic hydrocarbons and normal alkanes, and on the age of the leaks from which the unknown samples originated. Secondly, the method of fluorescence spectroscopy was applied, focusing on the fluorescence of the aromatic components of each sample for different wavelengths. Finally, using multivariate techniques, in particular principal component analysis (PCA), a classification of all samples and a correlation of the unknowns with pure petroleum products was performed.