The efficient utilization of water resources is the key to ensuring sustainable development. Due to the complex relationship between resource utilization and economy and the environment, there are positive societal effects from a scientific and precise assessment of the carrying capacity of water supplies. This study aims to investigate the uncertainty associated with the selection of evaluation parameters in assessing the carrying capacity of water resources. To achieve this, the fuzzy comprehensive evaluation model is adopted, and two distinct weighting methods, namely hierarchical analysis and entropy weighting, are applied to analyze the sources of uncertainty in the evaluation results under the framework of the established evaluation indicators. Aiming at the traditional water resources carrying capacity, evaluation indexes are redundant and the correlation is not very close. Thus, the sensitivity analysis method based on the weights of the indexes is proposed to eliminate the indexes that have the greatest impact in order to decrease the uncertainty of the evaluation results. The results indicate that the correlationship coefficient of the comprehensive evaluation results obtained through the two weighting ways is 0.4542, which is not a large correlation, so the uncertainty of the assignment of indicator weights exists. The calculation of the sensitivity index shows that the weights of the three indicators of the utilization ratio of water resources development, water consumption per unit of GDP and per capita water resources are the most sensitive, which are 40.62%, 27.58%, and 23.61%, respectively, and these are the key influencing factors. This demonstrates that improving the accuracy of the primary control indices and the quality control of weight assignment can assist with lowering the error of the carrying capacity assessment of water resources and also point the fuzzy evaluation model in the right direction.

Groundwater nitrate (NO₃⁻) pollution is a critical environmental challenge with direct implications for human health. In this work, we propose a comprehensive analytical framework that integrates multi-model intercomparison, interpretable machine learning techniques, and quantitative health risk evaluation to tackle the pressing groundwater nitrate governance dilemmas in Handan City, a representative urban area in North China. Based on 157 groundwater samples and 17 hydrochemical parameters, comparative analysis of three state-of-the-art machine learning algorithms showed that the Light Gradient Boosting Machine (LightGBM) algorithm outperformed all counterparts, delivering the optimal predictive performance (R² = 0.753, RMSE = 3.67). SHapley Additive exPlanations (SHAP) analysis identified F⁻, Ca²⁺, Cl⁻, K⁺, total hardness, and Mg²⁺ as dominant factors influencing groundwater NO₃⁻ concentrations, reflecting the combined effects of carbonate dissolution, nitrification, and anthropogenic inputs. Subsequently, we performed a health risk assessment based on the standard methodological framework issued by the United States Environmental Protection Agency (USEPA), and the results indicated that children were the most vulnerable group, with hazard quotient (HQ, a non-carcinogenic risk indicator) values reaching 1.07 in the western mountainous region, exceeding the safety threshold (HQ > 1). These findings clarify the pollution mechanisms and spatial heterogeneity, and provide targeted policy guidance for groundwater protection as well as the safeguarding of public health.