Contaminated food through dietary intake has become the main potential risk impacts on human health. This study investigated concentrations of rare earth elements (REEs) in soil, vegetables, human hair and blood, and assessed human health risk through vegetables consumption in the vicinity of a large-scale mining area located in Hetian Town of Changting County, Fujian Province, Southeast China. The results of the study included the following mean concentrations for total and bio-available REEs of 242.92 ± 68.98 (135.85-327.56)μg g(-1) and 118.59 ± 38.49 (57.89-158.96)μg g(-1) dry weight (dw) in agricultural soil, respectively, and total REEs of 3.58 ± 5.28 (0.07-64.42)μg g(-1) dw in vegetable samples. Concentrations of total REEs in blood and hair collected from the local residents ranged from 424.76 to 1274.80 μg L(-1) with an average of 689.74 ± 254.25 μg L(-1) and from 0.06 to 1.59 μg g(-1) with an average of 0.48 ± 0.59 μg g(-1) of the study, respectively. In addition, a significant correlation was observed between REEs in blood and corresponding soil samples (R(2)=0.6556, p<0.05), however there was no correlation between REEs in hair and corresponding soils (p>0.05). Mean concentrations of REEs of 2.85 (0.59-10.24)μg L(-1) in well water from the local households was 53-fold than that in the drinking water of Fuzhou city (0.054 μg L(-1)). The health risk assessment indicated that vegetable consumption would not result in exceeding the safe values of estimate daily intake (EDI) REEs (100-110 μg kg(-1)d(-1)) for adults and children, but attention should be paid to monitoring human beings health in such rare earth mining areas due to long-term exposure to high dose REEs from food consumptions.

Pigments are of great importance in physiological and ecological processes of plants. Revealing the relationships between their characteristic bands in the spectral reflectance and the contents of photosynthetic pigments using hyperspectral data will help to understand the spectral reflectance characteristics of photosynthetic pigments and provide the technical supports for rapid non鄄destructive monitoring of plant photosynthetic pigments using hyperspectral remote sensing. In this paper, eucalyptus leaf samples collected in field were used to measure the hyperspectral reflectance of the leaves and the corresponding contents of chlorophyll, carotenoid in our laboratory. The spectral data were processed using spectral analysis techniques and analyzed using statistical methods to extract spectral characteristic parameters and subsequently to establish the estimation models of spectral characteristics and both chlorophyll and carotenoid contents. Precision tests proved that the best model is the exponential model using ( SDr-SDb) / ( SDr+SDb) as a variable, where SDr and SDb are red and blue edge areas, respectively.