Improving a herbicide risk assessment model in paddy rice cultivation.

Herbicides play a pivotal role in paddy rice cultivation by effectively controlling weeds, thus ensuring optimal resource utilisation and higher crop yields, making them indispensable for efficient rice production systems. However, herbicide applications could be related to potential environmental impacts such as water contamination and harm to non-target species, requiring special attention in their management to ensure the long-term sustainability of rice farming practices. The development and utilisation of robust risk assessment indicators for pesticides are essential tools in evaluating and mitigating potential environmental and human health hazards associated with pesticide use in agricultural practices. The Environmental Potential Risk Indicator for Pesticides (EPRIP) is not suitable for rice paddy cultivation due to its limitations in accurately assessing pesticide risk in this specific agricultural context. This is primarily attributed to the unique hydrological characteristics and ecosystem dynamics of paddy fields, which significantly differ from other agricultural systems. To address this issue and to enhance the accuracy of pesticide risk assessment in rice paddy fields, EPRIP has been improved and validated in two agricultural seasons. A synergistic approach involving field experiments and enhanced EPRIP model simulations was employed to assess the risk associated with the application of two herbicides in Italian paddy rice systems. The observed and model-predicted surface water (SW) concentrations exhibited a close alignment, though an overestimation was observed for groundwater (GW). In general, the estimated Risk Points (1 for SW and 4 for GW) were largely in accord with those derived from the field experiments (1 for SW and 3 for GW), suggesting that the refined EPRIP model holds promise for conducting reliable risk assessments following herbicide applications in such contexts.

Alkaline-Phosphatase-Based Nanostructure Assemblies for Electrochemical Detection of microRNAs.

Different nanoarchitectures, rich in enzyme labels, are herein investigated for signal amplification in the electrochemical detection of nucleic acids and in particular of microRNAs. Dendritic amplification, accomplished by the use of streptavidin and biotinylated alkaline phosphatase, and enzyme-decorated liposomes are used as labels to amplify the microRNA-sensing, by their association to the probe-microRNA hybrid generated onto a gold transducer. Differential pulse voltammetry and faradaic impedance spectroscopy were employed to characterize these different amplification routes.