Near Infrared Spectroscopy of Fuel Contaminated Sand and Soil. I. Preliminary Results and Calibration Study

Regional areas may be contaminated by the past activities of the chemical and oil industries and of the military. Therefore, the present study was undertaken to test the possibilities of near infrared (NIR) reflectance spectroscopy for direct detection and determination of oil and fuel contaminations. If reliable results are obtained NIR reflectance spectroscopy could be a valuable part of land remediation processes. Preliminary investigations showed that it is possible to distinguish samples of stone chippings, sand, cultivated soil, humus and potting soil by multivariate data analysis. After spiking with gasoline, diesel, motor oil and synthetic hydrocarbon mixtures (BTEX) sand rather than cultivated soil shows obvious spectral absorptions due to contaminations higher than 1% (w/w). The influence of particle size fractions has been investigated in detail using dry sand sieved to < 500 μm (fine), 500–800 μm (medium) and >800 μm (coarse). Contaminations in fine and medium fractions often can be modelled with only one intensity at sufficiently low calibration error, SEC. With coarse fractions SEC is three times higher. Models based on derivative spectra have no significant advantage. In general, mean centring results in more pronounced error minima than multiplicative scatter correction (MSC). Partial least squares (PLS) models can be fitted to obtain any wanted SEC even by cross-validation. For comparable SEC, PLS models in general do not need more factors if samples become more inhomogeneous. Data pre-processing techniques such as Kubelka–Munk transformation, Saunderson correction, MSC and combinations thereof have been tested. Adequate sample variation of the diffuse reflectance fraction of detected light according to the Saunderson model could improve the performance of calibration models. The best values for standard error of prediction, SEP, are obtained if calibration models are derived from sets of spectra of sieved samples and used for contamination prediction of natural samples, and not vice versa. Spectra of contaminated soil and humus need cleverer spectral selection and pre-processing for better performance of calibration models.