| dc.description.abstract | Expanding applications of metallic nano-agrichemicals have resulted in their increasing accumulation in agricultural soils. In addition to their potential uptake by agricultural crops, the effects of engineered nanoparticles (ENPs) on soil properties and the fate of co-contaminants in soil plant systems are still poorly understood.
The goal of this study was to gain better understanding on the impact of metallic ENPs on paddy soil health and their interactions with co-contaminants such as arsenic (As) and cadmium (Cd), as well as their plant uptake and accumulation. To achieve this goal, greenhouse experiments were performed to evaluate the effects of zinc oxide nanoparticles (ZnO NPs), copper oxide nanoparticles (CuO NPs), and silicon oxide nanoparticles (SiO2 NPs), and their bulk and ionic counterparts on the properties of paddy soil, the formation and properties of iron plaque and As accumulation in rice. A pot experiment was also conducted to investigate the effect of SiO2 NPs on the simultaneous uptake of As and Cd by rice seedlings with different water management schemes, because As and Cd co-contamination is common in paddy soils and both elements are hazardous to human health. Finally, machine learning was applied to predict plant uptake of metallic ENPs.
Our results showed distinctive effects of ENPs from their bulk and ionic counterparts on a range of soil properties, such as soil pH, redox potential, soil organic carbon (SOC), and cation exchange capacity (CEC). Highly intriguingly, our results for the first time showed that chemical amendments such as metallic ENPs affected iron plaque formation on rice root surface and consequently As accumulation in rice tissues. The impact of chemical amendments on iron plaque formation varied with the composition of ENPs and the growth stage of rice plants. Our results also showed that simultaneous reduction of As and Cd in rice shoots could potentially be achieved by proper combination of SiO2 NPs and water management. Furthermore, machine learning accurately predicted plant uptake and translocation of ENPs based on a set of input parameters that include representative properties of ENPs, plant species and soil properties. Overall, this study provided insight into safe and sustainable applications of ENPs in agriculture. | |
