“…Due to the various of mining sources and transporting modes, the formation and dispersion processes of heavy metal contamination were still hardly differentiated. The spatial distribution mapping of heavy metal contamination could be improved using GIS enhanced multiple linear regression in the future investigation 31 . Figure 1 Spatial distribution of heavy metal contents in surface soils in Jin-Qu basin (The map was created by ARCGIS 10.5, https://developers.arcgis.com/ ).…”
A regional field survey of a total of 109 pairs of soil and rice samples was conducted to evaluate the health risks posed by heavy metals in the Jin-Qu Basin, China. The studied soils are characterized by acid (pH in mean level of 5.5), carbon rich (soil organic matter in mean of 33.6 g kg
−1
) and mainly contaminated by Cd (42.2% samples exceeded the standard value of 0.3 mg kg
−1
(GB15618-2018)). The spatial distributions of Cd, Pb and Zn exhibited similar geographic trends. 34% and 30% of the rice samples containing Cd and Pb exceeded the threshold value of 0.2 mg kg
−1
(GB2762-2017), respectively. The risk estimation of dietary intake had a target hazard quotient value of Cd of 0.918 and a hazard index value for rice consumption of 2.141. Totally, Cd and Pb were found to be the main components contributing to the potential health risks posed by non-carcinogenic effects for local inhabitants.
“…Due to the various of mining sources and transporting modes, the formation and dispersion processes of heavy metal contamination were still hardly differentiated. The spatial distribution mapping of heavy metal contamination could be improved using GIS enhanced multiple linear regression in the future investigation 31 . Figure 1 Spatial distribution of heavy metal contents in surface soils in Jin-Qu basin (The map was created by ARCGIS 10.5, https://developers.arcgis.com/ ).…”
A regional field survey of a total of 109 pairs of soil and rice samples was conducted to evaluate the health risks posed by heavy metals in the Jin-Qu Basin, China. The studied soils are characterized by acid (pH in mean level of 5.5), carbon rich (soil organic matter in mean of 33.6 g kg
−1
) and mainly contaminated by Cd (42.2% samples exceeded the standard value of 0.3 mg kg
−1
(GB15618-2018)). The spatial distributions of Cd, Pb and Zn exhibited similar geographic trends. 34% and 30% of the rice samples containing Cd and Pb exceeded the threshold value of 0.2 mg kg
−1
(GB2762-2017), respectively. The risk estimation of dietary intake had a target hazard quotient value of Cd of 0.918 and a hazard index value for rice consumption of 2.141. Totally, Cd and Pb were found to be the main components contributing to the potential health risks posed by non-carcinogenic effects for local inhabitants.
“…Due to the toxic effects, long-term accumulation, and bio-magnification characteristics, heavy metal pollution, even at low concentrations, has attracted widespread attention. Heavy metals are considered one of the most critical toxicants among the multilayered soil and environmental pollutants [213,214]. The existence of heavy metals in the ecosystem increases the potential intake of such toxic components by the living organisms and their accumulation in many body organs, including kidney, liver, bone, etc.…”
Section: Effect Of Heavy Metals and Pesticides Toxicity On Human Healmentioning
Environmental problems have always received immense attention from scientists. Toxicants pollution is a critical environmental concern that has posed serious threats to human health and agricultural production. Heavy metals and pesticides are top of the list of environmental toxicants endangering nature. This review focuses on the toxic effect of heavy metals (cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn)) and pesticides (insecticides, herbicides, and fungicides) adversely influencing the agricultural ecosystem (plant and soil) and human health. Furthermore, heavy metals accumulation and pesticide residues in soils and plants have been discussed in detail. In addition, the characteristics of contaminated soil and plant physiological parameters have been reviewed. Moreover, human diseases caused by exposure to heavy metals and pesticides were also reported. The bioaccumulation, mechanism of action, and transmission pathways of both heavy metals and pesticides are emphasized. In addition, the bioavailability in soil and plant uptake of these contaminants has also been considered. Meanwhile, the synergistic and antagonistic interactions between heavy metals and pesticides and their combined toxic effects have been discussed. Previous relevant studies are included to cover all aspects of this review. The information in this review provides deep insights into the understanding of environmental toxicants and their hazardous effects.
“…where i y =measured value, i ŷ =predicted value, y =mean measured value, and n = the number of sample in the test set with i=1,2,...,n. According to the model accuracy assessment theory proposed by Williams [38] and Saeys [39], the criteria to evaluated the model prediction ability was as follows: the values of R 2 and RPD are great than 0.9 and 3.0 represents the model was excellent in prediction; good prediction is defined by R 2 value at 0.82 to 0.9 and RPD value at 2.5 to 3.0; approximate prediction is with R 2 value at 0.66 to 0.81 and RPD values at 2.0 to 2.5; the possibility to distinguish high and low values is identified with R 2 value at 0.50 to 0.65 and RPD values at 1.5 to 2.0; meanwhile, not only is the value of R 2 greater than 0.5, but also the RPD value is more than 1.5 is recognized as a successful model. A model with good robustness is supposed to meet high R 2 and RPD values, and low RMSE values in prediction accuracy. All model construction and accuracy evaluation were performed in MATLAB 2014a.…”
Section: E Model Calibration and Evaluationmentioning
Benefiting from the nanoscale sampling intervals and subtle spectral information in the visible and near-infrared band, hyperspectral technology is considered as an efficient means for monitoring soil heavy metal contamination whereby the good robustness of prediction model is driven by the increase to spectral dimension in model analysis. Considering the positive correlation between samples size and spectral dimension, we focuses on a novel derivation of enlarging samples size in this study to improve model performance by i) preparing artificial samples taking into account of flexibility and control over the laboratory environment compared with collecting wild samples, and ii) using transfer learning method called transfer component analysis (TCA) for reducing spectral feature differences caused by soil heterogeneity to train model in the same data distribution. The proposed approach was tested on three heavy metals, namely copper (Cu), cadmium (Cd) and lead (Pb), collected in the mining area located in the Xiangjiang Basin, Hunan Province, China. The experiments showed that the initial model constructed by a small number of wild samples performed strong prediction sensitivity as the training samples change. In contrast, a modified model with TCA could showed good robustness with excellent predicted ability, the average prediction accuracy of the determinable coefficient (R 2) and the ratio of prediction to deviation (RPD) improved to 0.73 and 1.90, 0.74 and 1.92, 0.72 and 1.73, respectively. The results illustrated there was a more reliable modeling method in potential to predict soil heavy metals based on hyperspectral analysis at low cost.
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