Estimate of Heavy Metals in Soil Using Combined Geochemistry and Field Spectroscopy in Miyi Mining Area

Heavy metal-contaminated soil and water is a major environmental issue in the mining areas. However, as the heavy metals migrate frequently, the traditional method of estimating the soil’s heavy metal content by field sampling and laboratory chemical analysis followed by interpolation is time-consuming and expensive. This chapter intends to use field hyperspectra to estimate the heavy metals in the soil in Bai-ma, De-sheng and YuanBaoshan mining areas, Miyi County, Sichuan Province. By analyzing the spectra of soil, the spectral features derived from the spectra of the soils can be found to build the models between these features and the contents of Mn and Co in the soil by using the linear regression method. The spectral features of Mn are 2142 and 2296 nm. The spectral features of Co are 1918, 1922 and 2205 nm. With these feature spectra, the best models to estimate the heavy metals in the study area can be built according to the maximal determination coefficients (R2). The determination coefficients (R2) of the models of retrieving Mn and Co in the soil are 0.645 and 0.8, respectively. The model significant indexes of Mn and Co are 2.04507E-05 and 7.73E-06. These results show that it is feasible to predict contaminated heavy metals in the soils during mining activities for soil remediation and ecological restoration by using the rapid and cost-effective field spectroscopy

Estimating regional heavy metal concentrations in rice by scaling up a field-scale heavy metal assessment model

National Natural Science Foundation of China [40771155]; National High-Tech R&D Program of China [2007AA12Z174]The objective of this study was to determine the levels of heavy metals cadmium (Cd) and copper (Cu) in rice by upscaling a field-scale heavy metal assessment (FHMA) model from field to regional scale. The FHMA model was established on the basis of spectral parameters in combination with soil parameters by employing a generalized dynamic fuzzy neural network. The piecewise function and ordinary kriging were developed to suit the upscaled spectral parameters and soil parameters, respectively. In addition, the network structure and fuzzy rules, which had already been developed in the FHMA model, would be subsequently extracted as those of the regional-scale heavy metal assessment (RHMA) model. The results showed that the latter performed well at prediction with a correlation coefficient (R-2) and model efficiency (ME) greater than 0.70, and can be applied to other areas, perhaps universally. This study suggests that it is feasible to accurately estimate regional heavy-metal concentrations in rice by scaling up the FHMA if such a strategy is appropriately selected and finds that the piecewise function is well suited to transferring spectral data from a field to a regional scale. (c) 2012 Elsevier B.V. All rights reserved