Abstract
With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.
Highlights
With the rapid development of social economies, industrialization is accelerating, leading to the increasing exploitation of resources and thereby a gradual deterioration of the ecological environment
Unlike the common method used in direct regression experiments on the target heavy metals and the band reflectivity of multi-spectral remote sensing images, our method involved the selection of several different types of spectral indexes as modeling factors
The results show that: (1) The optimized model improves the accuracy of the target heavy metal estimation model and provides the possibility of estimating the contents of heavy metals for which the spectral factors or spectral indexes are not significantly correlated
Summary
With the rapid development of social economies, industrialization is accelerating, leading to the increasing exploitation of resources and thereby a gradual deterioration of the ecological environment. The exploitation of resources by industrial and mining enterprises causes soil pollution, of which heavy metal pollution is the most prominent. Of ‘‘three wastes’’ (Waste water, waste gas, solid waste)by industrial and mining enterprises, the environment is exposed to several pollutants containing heavy metals. Because heavy metals are generally insoluble in water and are not decomposed by soil microorganisms, they accumulate and their concentrations may eventually exceed the content standard. Under the effects of wind and water circulation, heavy metals in soil may enter the atmosphere and different water bodies and result in air pollution, groundwater pollution, and ecosystem degradation.
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