Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6348718 | International Journal of Applied Earth Observation and Geoinformation | 2015 | 9 Pages |
Abstract
Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and the contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480Â nm, 500Â nm, 565Â nm, 610Â nm, 680Â nm, 750Â nm, 1000Â nm, 1430Â nm, 1755Â nm, 1887Â nm, 1920Â nm, 1950Â nm, 2210Â nm, 2260Â nm; The spectral features of Cu are: 480Â nm, 500Â nm, 610Â nm, 750Â nm, 860Â nm, 1300Â nm, 1430Â nm, 1920Â nm, 2150Â nm, 2260Â nm; And the spectral features of Cr are: 480Â nm, 500Â nm, 610Â nm, 715Â nm, 750Â nm, 860Â nm, 1300Â nm, 1430Â nm, 1755Â nm, 1920Â nm, 1950Â nm. With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R2. The R2 of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals' concentrations can be created by using the Inverse distance weighted (IDW).The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy.
Keywords
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Song Lian, Jian Ji, Tan De-Jun, Xie Hong-Bing, Luo Zhen-Fu, Gao Bo,