The alluvial-diluvial plain of southwest Shandong Province is an important agricultural economic zone and energy base in Shandong Province. Groundwater plays an extremely significant role in the development of the regional social economy. In this study, 50 sets of water samples, collected from 25 wells during October 2016 and June 2017, were utilized to determine the hydrogeochemistry and the suitability of groundwater in the alluvial-diluvial plain of southwest Shandong Province for different applications, such as drinking and irrigation. Most of the water samples could be classified as hard-fresh water or hard-brackish water, and the dominant water types were HCO₃-Na and mixed types. Water-rock interactions and evaporation were the dominant controlling factors in the formation of the hydrochemical components in the groundwater. Dissolutions of silicate, calcite, dolomite, and gypsum are the major reactions contributing and defining the groundwater chemistry in this plain. Moreover, cation exchange is a non-negligible hydrogeochemical process in this plain. Calculated saturation index (SI) values indicate that aragonite, calcite and dolomite are saturated, while the SI values for gypsum and halite are unsaturated. Based on fuzzy comprehensive evaluation, the groundwater quality ranges from excellent to very poor. More than 50% of all groundwater samples from 2016 are categorized as poor or very poor, suggesting that the water from these wells is not suitable for drinking. According to the sodium adsorption ratio and percentage sodium, most of the samples are suitable for agricultural irrigation. Overall, the quality of the groundwater in 2017 was found to be better than in 2016.

To assess and predict the Nansi Lake soil pollution risk, we evaluate the soil environmental quality in the Nansi Lake region using machine learning techniques, combined with the SHapley Additive exPlanations (SHAP) model for interpretability. The primary objective was to predict the level of soil pollution caused by heavy metals, incorporating the traditional Pollution Load Index (PLI) and Potential Ecological Risk Index (PERI) methods. Through the integration of statistical characteristics, PLI, and PERI evaluations, a new assessment method was created, categorizing soil pollution into “Class0—no risk”, “Class1—low risk”, and “Class2—high risk”. Various machine learning models, including Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest (RF), and XGBoost, were employed to predict the soil quality based on these indices. XGBoost demonstrated the highest accuracy, achieving a prediction accuracy of 93%. SHAP analysis was further applied to explain the machine learning model and determined that the accumulation of key pollutants such as cadmium (Cd) and mercury (Hg) may significantly produce soil pollution risk, and targeted management needs to be developed for these pollution features.

This study analyzed the sources of total metal elements using the positive matrix factorization (PMF) model and conducted human health risk assessment for adults and children using the health risk assessment model recommended by the United States Environmental Protection Agency (USEPA). According to the health risk assessment, As is the main contributor to the non-carcinogenic risk of groundwater in Tengzhou, with drinking water as the main exposure route. Regarding carcinogenic risks (CR), the values of As and Cr for adults and children were higher than 1 × 10⁻⁴, with drinking water as the main exposure route. Therefore, As is the largest contributor to the CR of groundwater for adults and children and drinking water is the main exposure route in the study area. The primary exposure pathways are oral intake and dermal contact, with oral intake presenting a significant risk. The carcinogenic risks according to principal component analysis (PCA) and PMF analysis showed that the main sources of heavy metals in shallow groundwater in Tengzhou City are agricultural, industrial, natural, and industrial deposition sources, with contribution rates of 21.7%, 27.2%, 31.0%, and 20.1%, respectively. In particular, natural sources are the largest contributor to the accumulation of heavy metals.

Geothermal water resources are a kind of clean energy, which is a renewable resource to a certain extent and has a high value of development and utilization. To understand the hydrochemical characteristics, origins, and temporal variations of geothermal water quality in Tangtou, 13 geothermal water samples from 2007 to 2019 and geothermal geological conditions were collected. Cl⁻ and Na⁺ are the major ions, which make the geothermal water belong Cl-Na type. The total dissolved solids values of geothermal waters were 1560–2512 mg/L and pH were in the range of 6.7–8.8. The development of faulted structures provides conditions for the formation of geothermal water. In addition, geothermal water is recharged by shallow underground cold water. Water-rock interaction, as well as mixing processes, in the process of long runoff path and the slower deep-water cycle is the main factor controlling the chemical composition of geothermal water. Calculated saturation index values indicated that the geothermal water was saturated with respect to silicate and carbonate minerals. Cation and silica chemical geothermometers indicated that reservoir temperatures ranged from 94.63 to 196.10 °C and from 69.13 to 123.75 °C, respectively. Based on the grey relational analysis, the main physicochemical components of geothermal water are obviously correlated with the geothermal water exploitation and precipitation. Overall, affected by exploitation and precipitation, main physicochemical components (such as the total dissolved solids, total hardness, temperature, Na⁺, Ca²⁺, Cl⁻, K⁺, and Mg²⁺) showed a certain stage.