Predicting heavy metal concentrations in soils and plants using field spectrophotometry

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R²~0.9, RPD~2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R²~0.7, RPD~1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R², rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.