Infrared spectroscopy and multivariate methods as a tool for identification and quantification of fuels and lubricant oils in soil

• Autor(a) principal: Nespeca, Maurílio Gustavo [UNESP]
• Data de Publicação: 2018
• Outros Autores: Piassalonga, Gabriel Baroffaldi [UNESP], de Oliveira, José Eduardo [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s10661-017-6454-9; http://hdl.handle.net/11449/170544
• topic: Fuel leakage; Genetic algorithm; Infrared spectroscopy; Multivariate filters; Partial least square; Soil contamination

Environmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great concern due to the presence of carcinogenic compounds in the composition of gasoline, diesel, and mineral lubricant oils. Chromatographic methods or non-selective infrared methods are usually used to assess soil contamination, which makes environmental monitoring costly or not appropriate. In this perspective, the present work proposes a methodology to identify the type of contaminant (gasoline, diesel, or lubricant oil) and, subsequently, to quantify the contaminant concentration using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and multivariate methods. Firstly, gasoline, diesel, and lubricating oil samples were acquired from gas stations and analyzed by gas chromatography to determine the total petroleum hydrocarbon (TPH) fractions (gasoline range organics, diesel range organics, and oil range organics). Then, solutions of these contaminants in hexane were prepared in the concentration range of about 5–10,000 mg kg−1. The infrared spectra of the solutions were obtained and used for the development of the pattern recognition model and the calibration models. The partial least square discriminant analysis (PLS-DA) model could correctly classify 100% of the samples of each type of contaminant and presented selectivity equal to 1.00, which provides a suitable method for the identification of the source of contamination. The PLS regression models were developed using multivariate filters, such as orthogonal signal correction (OSC) and general least square weighting (GLSW), and selection variable by genetic algorithm (GA). The validation of the models resulted in correlation coefficients above 0.96 and root-mean-square error of prediction values below the maximum permissible contamination limit (1000 mg kg−1). The methodology was validated through the addition of fuels and lubricating oil in soil samples and quantification of the TPH fractions through the developed models after the extraction of the analytes by the EPA 3550 method adapted by the authors. The recovery percentage of the analytes was within the acceptance limits of ASTM D7678 (70–130%), except for one sample (69% of recovery). Therefore, the methodology proposed here provides faster and less costly analyses than the chromatographic methods and it is adequate for the environmental monitoring of soil contamination by gas stations.