Spinacia oleracea L., as the most widely cultivated green leafy vegetable in China, can threaten human health in cases of its excessive heavy metal content, especially in mining areas of karst landforms. Therefore, the present study mainly investigates whether biochar is useful for remediating heavy metal pollution in soil and S. oleracea and the degree of this improvement in karst areas. The effects of heavy metal exposure on the health of children and adults in S. oleracea and rhizosphere lime soil with six biochar levels are evaluated by a health risk assessment, namely, 4000 g of lime soil (C-0), 160 g of biochar + 3840 g of lime soil (C-160), 240 g of biochar + 3760 g of lime soil (C-240), 320 g of biochar + 3680 g of lime soil (C-320), 400 g of biochar + 3600 g of lime soil (C-400) and 800 g of biochar + 3200 g of lime soil (C-800). The results show that the pH values of the lime soil were positively correlated with Pb, P and K contents and negatively correlated with As, Cr, Hg, Cd and N contents in S. oleracea. The assessments of the potential ecological risk index show that the soil samples for the C-0 and C-160 levels pose moderate ecological hazards, while the soil samples for the C-320, C-800, C-400 and C-240 levels constitute mild ecological hazards. The single noncarcinogenic risks, total noncarcinogenic risk indexes, single carcinogenic risks and total carcinogenic risks values indicate that exposure to heavy metals in lime soil and S. oleracea poses a serious threat to human health. It also presents an unacceptable cancer risk and children are more threatened than adults. Our results suggest that heavy metal pollution of S. oleracea and its rhizosphere lime soil in karst areas still poses a threat to human health after adding biochar, and the relevant local departments need to implement more active measures to solve the excessive heavy metal contents in the local soil and vegetables of this karst regions.
Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks
