Feasibility of Reflectance Spectroscopy for the Assessment of Soil Mercury Contamination

Conventional methods for investigating soil Hg contamination based on raster sampling and chemical analysis are time-consuming and relatively expensive. The objective of this study was to develop a rapid method for investigating Hg concentration in suburban agricultural soils of the Nanjing region using reflectance spectra within the visible-near-infrared (VNIR) region. Several spectral pretreatments (absorbance, Kubelka−Munk transformations and their derivatives) were applied to the reflectance spectra to optimize the accuracy of prediction. The prediction of Hg concentration was achieved by univariate regression and principal component regression (PCR) approaches. The optimal model (R = 0.69, RMSEP = 0.15) for predicting Hg was achieved using the PCR method with the Kubelka−Munk transformation as the spectral predictor. Comparison of three wavelength ranges (0.38−1.1, 1.0−2.5, and 0.38−2.5 μm) on the effect of prediction accuracy showed that the best results were acquired using the 1.0−2.5 μm spectral region. Correlation analysis revealed that Hg concentration was negatively correlated with soil reflectance while positively correlated with the absorption depths of goethite at 0.496 μm and clay minerals at 2.21 μm, suggesting that Hg-sorption by clay-size mineral assemblages in soils was the mechanism by which to predict spectrally featureless Hg. These results indicate that it is feasible to predict Hg levels in agricultural soils using the rapid and cost-effective reflectance spectroscopy. Future study with operational remote sensing techniques and field measurements is strongly recommended.