Improper cultivation can easily cause excessive nitrate accumulation in leafy vegetables, and the cooking processes used to prepare them can upset their nitrate/antioxidant balance, affecting their potential nitrite safety risk (PNSR). We investigated the impacts stir-frying, steaming, microwaving, and boiling on the nitrate, nitrite, and antioxidant capacity in water spinach and cabbage, and observed the impacts of storage duration on the PNSR. The antioxidant/in vivo nitrite ratio (A/N) was used to evaluate the nitrite risks in the cooked vegetables. Boiling achieved the highest A/N ratio (1.57) for water spinach, reducing the nitrate content by 25% without significantly affecting the antioxidant capacity. Stir-frying achieved the highest A/N ratio (6.55) for cabbage, increasing the antioxidant capacity by 140% without significantly affecting the nitrate content. Furthermore, it was found that the storage periods for boiled water spinach and stir-fried cabbage should not exceed 12 h and 24 h, respectively. Appropriate cooking methods and limited storage times are thus required for leafy vegetable to prevent adverse health effects.

Understanding the characteristics of biogas demand in rural areas is essential for on-demand biogas production and fossil fuel offsetting. However, the spatiotemporal features of rural household energy consumption are unclear. This paper developed a rural biogas demand forecasting model (RBDM) based on the hourly loads of different energy types in rural China. The model requires only a small amount of publicly available input data. The model was verified using household energy survey data collected from five Chinese provinces and one year’s data from a village-scale biogas plant. The results showed that the predicted and measured biogas consumption and dynamic load were consistent. The relative error of village biogas consumption was 11.45%, and the dynamic load showed seasonal fluctuations. Seasonal correction factors were incorporated to improve the model’s accuracy and practicality. The accuracy of the RBDM was 19.27% higher than that of a static energy prediction model. Future research should verify the model using additional cases to guide the design of accurate biogas production and distribution systems.