Volatility characteristics and hyperspectral-based detection models of diesel in soils

This study developed an efficient method using hyperspectral camera for detecting diesel content in soils with spectral indices. Over 70 days of the experiment, clean soils were saturated with diesel, and 186 measurements were taken to monitor the evaporation rate and spectral variation. The diesel evaporation followed a logarithmic pattern, where the diesel volatility decreased from 1.57% per day during the initial period to 0.06% per day during the late period. Using the hull-quotient reflectance at 2236 nm, the diesel content prediction model derived from a stepwise multiple linear regression (SMLR) achieved satisfactory accuracy with sufficient statistical significance (R 2 = 0.89, RPD = 2.52). This spectral band was well visualized for diesel presence in hyperspectral images as the band infers variations in two absorptions (CH/AlOH and CH) concurrently. Additionally, this study presented an age estimation model based on the diesel evaporation rate using the same spectral band. Given the fact that this study is based on the largest number of samples with the longest observation period and models were developed excluding atmospheric absorption bands, the simple form of the spectral index makes it applicable to large-scale diesel pollution detection with hyperspectral scanners or narrow-band multispectral cameras in real-world cases. • Hyperspectral imaging visualized the diesel content in the soil as it evaporated. • Diesel showed a logarithmic evaporation pattern over 70 days with spectral changes. • A 2236 nm hull-quotient model detected diesel content from 31.9% to 59.7%. • An age estimation model was proposed based on spectral characteristics. • This technique applies to multi-scale diesel detection for quick assessment.