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Aims: Atmospheric trace metal deposition is an important source in agricultural soils and crops, but a large knowledge gap exists between foliar and root uptake of wet/dry deposition. We aim to distinguish the contribution of dry and wet-deposited trace metals to rice plant via foliar and root uptake and to investigate their accumulations during the full growth season. Methods: We performed an atmospheric exposure experiment using customized open top chambers (OTCs) to distinguish the impacts of wet and dry deposition contribution to foliar and root uptake and their mobility in soils. The foliar uptake and then translocation was hypothesized the major pathway of trace metal accumulation in rice grains. Results: Foliar uptake of wet- and dry-deposited Cu, Zn, and Pb played an important role in their accumulations in rice plant, contributing to 44–47%, 23–25%, and 35–37% in rice grains, respectively. The leaf Cu, Zn, and Pb accumulated from atmospheric deposition can be transported upward to rice grains but nodes seem to restrict them downward translocation. Rice leaves cannot directly take up atmospheric As and the deposited As can only accumulate in rice through the root uptake. The trace metals showed high mobility in atmospheric deposition, which significantly increase the bioavailable trace metal fractions in surface soils and soil solutions. Conclusions: This study provides a new insight of similar or even higher contributions of foliar uptake than root uptake of trace metals from atmospheric deposition and that reducing the accumulation of atmospheric trace metals due to deposition on leaves can effectively decrease the accumulation of trace metals in crops. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • A 3-year study of Cd mass balances were performed by an accurate plot experiment near a smelter. • Irrigation-water Cd input contributed 55–61% to background and moderately polluted cultivated soils. • Surface-runoff and leaching-water outputs were the main output pathways of Cd in agroecosystem. • Rice grain Cd were accuracy predicted by GA-BP neural network based on mass balance factors. [ABSTRACT FROM AUTHOR]