Inefficient and non-environmentally friendly absorbent production can lead to much resource waste and go against low carbon and sustainable development. A novel and efficient Mg-Fe-Ce (MFC) complex metal oxide absorbent of fluoride ion (F⁻) removal was proposed for safe, environmentally friendly, and sustainable drinking water management. A series of optimization and preparation processes for the adsorbent and batch experiments (e.g., effects of solution pH, adsorption kinetics, adsorption isotherms, effects of coexisting anions, as well as surface properties tests) were carried out to analyze the characteristics of the adsorbent. The results indicated that optimum removal of F⁻ occurred in a pH range of 4–5.5, and higher adsorption performances also happened under neutral pH conditions. The kinetic data under 10 and 50 mg·g⁻¹ were found to be suitable for the pseudo-second-order adsorption rate model, and the two-site Langmuir model was ideal for adsorption isotherm data as compared to the one-site Langmuir model. According to the two-site Langmuir model, the maximum adsorption capacity calculated at pH 7.0 ± 0.2 was 204 mg·g⁻¹. The adsorption of F⁻ was not affected by the presence of sulfate (SO₄²⁻), nitrate (NO₃⁻), and chloride (Cl⁻), which was suitable for practical applications in drinking water with high F⁻ concentration. The MFC adsorbent has an amorphous structure, and there was an exchange reaction between OH⁻ and F⁻. The novel MFC adsorbent was proven to have higher efficiency, better economy, and environmental sustainability, and be more environmentally friendly.

Leakage occurs in rural water supply pipelines very often and its locating is quite demanding even for specialists, which could result in a poor operation efficiency of rural water supply projects and thus a low rural water supply guarantee rate. In view of this problem, the detection of leakage, as well as its prediction, is of great significance for the operation, maintenance, and administration of rural water supply projects. The traditional monitoring-warning systems for urban water distribution networks cannot be applied to rural water distribution networks, due to various limitations. Meanwhile, as with the traditional models, most new approaches based on machine learning such as the artificial neural network (ANN), probabilistic neural network (PNN), and statistical learning theory (SLT) do not fit rural water distribution networks much better, being unable to converge and force high-accuracy results with small sample sizes, which is a crucial demand to meet when dealing with rural water supply pipelines. Extreme gradient boosting (XGBoost), a model that specializes in small sample sizes and has a high generalization ability, was applied to a rural water supply project in Ningxia, China. In this study, a monitoring-warning system featuring both leakage locating and quantity estimation was established based on XGBoost. The accuracy and F1-score of the leakage locating model were 95% and 93%, respectively, while those of the leakage quantity model reached 96% and 97%, respectively. Furthermore, the pressure of monitoring stations could be obtained through the feature importance analysis enabled by XGBoost, which is essential for leakage warning. These results indicate that this system based on XGBoost could be a promising solution to the leakage issue in rural water supply projects, as a great inspiration for future developments in intelligent monitoring-warning systems, thus providing reliable approaches for the sustainable development of rural drinking water supply projects.