Study on the Hyperspectral Retrieval and Ecological Risk Assessment of Soil Cr, Ni, Zn Heavy Metals in Tailings Area.

The large-scale rapid monitoring of heavy metal pollution has become a hot topic due to increasing contamination of Tailings soil by heavy metal. In order to explore the possibility of using soil spectrum to estimate the content of heavy metals in soil and realize the rapid monitoring of soil heavy metals in the Yangshanchong tailings area in Tongling, China. The spectral reflectance of soil and the content of heavy metals (Cr, Ni, Zn) in soil were determined. The optimal bands of Cr, Ni and Zn elements in soil appeared at 467 nm, 467 nm and 468 nm respectively, and the maximum correlation coefficients were - 0.716, - 0.685 and - 0.630. The inversion model of element Cr constructed under the Reciprocal Transformation Second Derivative has a better effect, and its determination coefficient R2 is 0.613; It is better to construct the model of elements Ni and Zn in the form of Reciprocal Transformation First Derivative, and their determination coefficients R2 are 0.724 and 0.603, respectively. The results of the single factor index method showed that the pollution degree of heavy metal elements in the soil in the study area is Ni > Zn > Cr; the Nemerow comprehensive pollution index method showed that the three elements in the study area were polluted to varying degrees, and the comprehensive pollution index was in order Ni > Zn > Cr; Comprehensive potential ecological hazard index evaluation, the pollution degree and ecological risk of the study area were low.

Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China.

Net primary productivity (NPP) of terrestrial ecosystems is an important metric of ecosystem functioning; however, the understanding of response mechanism of NPP to influencing factors and driving mechanisms are still limited. In this study, taking Anhui province as an example, spatio-temporal changes of NPP and its response to influencing factors were investigated for evaluating the effects of climate change and land use and land cover change (LUCC) on regional NPP. The Carnegie-Ames-Stanford Approach (CASA) model was employed for NPP simulation by using the MODIS normalized difference vegetation index (NDVI) data and meteorological data over 2001-2016. Combined domestic LUCC, the spatiotemporal distribution pattern and dynamic change characteristics of NPP under a long time series and its response to climate factors and human activities were analyzed in the Anhui province. The results indicated that from 2001 to 2016, total NPP had a fluctuated and decreased trend with the variation range between 30.52 and 38.07 TgC in Anhui province. The multi-year average of total NPP was about 34.62 TgC. The highest value was in 2008 and the lowest value was in 2011. Among them, amount of forestland NPP was the most. The spatial distribution of NPP shows that the high value area was mainly distributed in southern Anhui mountain areas and western Anhui Dabie mountain areas; the lower value was distributed in the middle in the study area. The area of which the NPP showed a slight decrease and essentially unchanged accounted for 59.35% and 31.82%, respectively. In general, the correlation between vegetation NPP and temperature was greater than that between precipitation. The vegetations NPP of eight land use types were all positively correlated with temperature. However, the other seven types of land use were negatively correlated with precipitation except cultivated land. In the past 16 years, the decrease of cultivated land areas and the increase of urban and construction land areas contributed a lot to the decrease of vegetation NPP in Anhui province. The NPP changes of different land use types were closely related to climatic factors, land cover area, and vegetation types.