Stable Isotope Ratios Trace the Rice Uptake of Cadmium from Atmospheric Deposition via Leaves and Roots.

Cadmium (Cd) stable isotopes provide a novel technique to investigate the fate of Cd in the environment, but challenges exist for tracing the sources in the plants. We performed individual rice leaf and root exposures to dry and wet deposition using customized open-top chambers (OTCs) in the greenhouse and in the field next to a smelter, respectively. The field experiment also included a control without Cd deposition and a "full" treatment. The exposure experiments and isotope signatures showed that leaves can directly take up atmospheric Cd and then translocate within rice plants to other tissues, contributing 52-70% of Cd in grains, which exceeded the contribution (30-48%) by root exposure. The Cd isotopes in leaves, nodes, internodes, and grains demonstrate that roots preferentially take up Cd from wet deposition, but leaves favor uptake of Cd from dry deposition. The Cd uptake by leaves is redistributed via nodes, allowing for upward transport to the grains but preventing downward transport to the roots. Leaves favor uptake of heavy isotopes from atmospheric deposition (ΔCd114/110Leaf-Dust: 0.10 ± 0.02‰) but retain light isotopes and transport heavy isotopes to the nodes and further to grains. These findings highlight the contribution of atmospheric deposition to rice and Cd isotopes as a useful tracer for quantifying sources in plants when different isotopic compositions are in sources.

Machine learning method for predicting cadmium concentrations in rice near an active copper smelter based on chemical mass balance.

Identification the sources of heavy metals can effectively control and prevent agricultural soil pollution. Here we performed a three-year mass balance study along a gradient of soil pollution near a smelter to quantify the potential contribution and net cadmium (Cd) fluxes and predict Cd concentration in rice grains by multiple regression (MR) and back propagation (BP) neural network. The Cd inputs were mainly from the irrigation water (54.6-60.8%) in the moderately polluted and background sites but from atmospheric deposition (90.9%) in the highly polluted site. The Cd outputs were mainly from the surface runoff (55.8-59.5%) in the moderately polluted and background sites, but from Sedum plumbizincicola phytoextraction (83.6%) in the highly polluted site. The soil Cd concentrations, the annual fluxes of atmospheric deposition, pesticides and fertilizers, irrigation water, surface runoff, and leaching water were selected as the dependent factors to predict Cd concentrations in rice grains. The genetic algorithms (GA)-BP neural network model gives the best prediction accuracy compared to the BP neural network model and multivariate regression analysis. The major implication is that the health risks through the consumption of rice can be rapidly assessed based on the Cd concentrations in rice grains predicted by the model.