A mixture of the environmentally friendly biosurfactants rhamnolipids and sophorolipids was used as a source of micelles in this study. The Box-Behnken design and response surface methodology was used to investigate the influence of factors on micellar-enhanced ultrafiltration (MEUF). Simulated Cd-containing wastewater was used for testing. Based on single-factor experiments, the initial Cd²⁺ concentration, biosurfactant mixing ratio (α) and pH were chosen as influential variables, and both the Cd²⁺ rejection coefficient and permeation flux were used as responses. A predictive model based on a quadratic polynomial regression equation was established to determine the optimized enhanced ultrafiltration conditions for Cd. The results show that the regression equation is extremely significant and fits the data accurately. The optimal enhanced ultrafiltration conditions are as follows: initial Cd²⁺ concentration of 10.0 mg/L, α of 0.30 and pH of 9.58. Under these conditions, the rejection coefficient and the permeation flux of Cd²⁺ are 99.14% and 37.36 L/m²·h, respectively. The experimental results confirm that the experimental values agree well with the values predicted by the model. Further, these results provide theoretical support for using MEUF to treat heavy metal-containing wastewater when biosurfactants are used for micelle formation.

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (<10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.