Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy

Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R2) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD1) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal spectra for Zn, while PCR equation employed RD1 values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area.