In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: “Excellent”, “Good”, “Medium”, “Poor”, or “Unacceptable”. Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, COD_Mn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking.

Accurate evaluation of groundwater quality and identification of key characteristics are essential for maintaining groundwater resources. The purpose of this study is to strengthen water quality evaluation through the SHAP and XGBoost algorithms, analyze the key indicators affecting water quality in depth, and quantify their impact on groundwater quality through interpretable tools. The XGBoost algorithm shows that zinc (0.183), nitrate (0.159), and chloride (0.136) are the three indicators with the highest weight. The SHAP algorithm shows that zinc (34.62%), nitrate (17.65%), and chloride (16.98%) have higher contribution values, which explains the output results of XGBoost. According to the calculation scores and classification standards of the water quality model, 49% of the groundwater samples in the study area have excellent water quality, 33% of the samples are better, and 18% of the samples are polluted. The results of positive matrix factorization (PMF) show that natural conditions, metal processing, metal smelting and mining, and agricultural activities all cause pollution to groundwater. Zinc, chloride, nitrate, and manganese were the key variables determined by the SHAP algorithm to explain the vast majority of human health risk sources. These findings indicate that interpretable machine learning not only improves the correlation of water quality assessment but also quantifies the judgment basis of each sample and helps to track key pollution indicators.

The problem of antibiotic contamination in the environment has attracted much attention in recent years. However, studies on antibiotic contamination in groundwater have only emerged in the last 15 years. In this study, we systematically reviewed the detection methods, distribution characteristics, risk, fate, and sources of antibiotics in groundwater in China, listed the concentrations of the main antibiotic types, and obtained the maximum concentrations by comparing the literature published in the last 10 years. The results show that 65 antibiotics were detected in groundwater in China, with sulfonamides and quinolones receiving the most attention. Antibiotic concentrations are influenced by hydrogeological conditions and seasonal variations, and the ecological risk in most areas is low to medium risk, which is relatively manageable. The highest concentrations found in most of the literatures were in the range of 10–1000 ng/L, but the maximum concentration can reach 47,444.5 ng/L, which requires extra attention. In addition, this study makes recommendations for improving groundwater monitoring surveys and protection measures to prevent the antibiotic contamination of groundwater more effectively.

Water pollution treatment technology assessment methods can be used to guide the selection of scientific and reasonable water pollution treatment technologies. At present, China has not yet established a standardized methodological system to scientifically evaluate these technologies, which makes it difficult to effectively screen water pollution treatment technologies suitable for specific watersheds or regions and restricts the sustainable development of local economy and society. In this study, an MDCA framework for water pollution treatment technologies was developed using a sustainable assessment approach. The framework begins with the definition of water pollution treatment technologies’ decision-making problems and then proceeds through the following: select potential water pollution treatment technologies; identify indicators; decision making; indicator scoring; indicator weighting; select appropriate assessment model; uncertainty analysis; and other steps to ultimately determine preferred options. To demonstrate the validity and applicability of the framework, typical urban wastewater treatment technologies were selected for case validation. The results showed that the comprehensive assessment results obtained by the multidimensional assessment model based on the ideal point method and weighted method were basically consistent. SBR and TAS can be used as recommended technologies for urban sewage treatment in the study area. However, these two technologies also have shortcomings, such as the unsatisfactory economic benefit of SBR, and the high sludge production and poor resistance to hydraulic shock loading of TAS. Among the six alternative technologies, CWS had the worst environmental benefit, mainly due to the low ammonia removal rate. A2/O has the worst economic and technical performance, mainly due to high investment and operation cost, relatively complex operation management, and poor resistance to hydraulic shock load. The method established in this study can not only select the technology, but also identify the shortcomings of the technology, therefore realizing the systematization and standardization.