Groundwater is the main source of water for agriculture, industry, and families in arid areas. At present, there is an urgent need to protect groundwater due to human activities. In this study, the Qingshui River Basin was selected as the study area. Based on the DRASTIC model, the DRASTIC-Land use type (DRASTICL) model and the analytic hierarchy process-DRASTICL (AHP-DRASTICL) model were constructed by optimizing the indicators and weights. And the three models were applied to calculate the groundwater vulnerability index (GVI), and the groundwater vulnerability map (GVM) was drawn. The validation results of Spearman correlation coefficient show that the DRASTICL model and the AHP-DRASTICL model have higher correlation, which indicates that the optimized model is more accurate. Among them, the AHP-DRASTICL model has the highest correlation coefficient (ρ = 0.92), which is more in line with the actual situation. The results of this study can provide scientific guidance for the protection and utilization of groundwater in the Qingshui River Basin. And it is of guiding significance for the study of groundwater vulnerability, especially for groundwater management in arid and semi-arid areas.

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of -0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from -0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas.

Groundwater is the main source of production and living in most arid and semi-arid areas, and it plays an increasingly critical role in achieving local urban development. There is a serious issue regarding the contradiction between urban development and groundwater protection. In this study, we used three different models to assess the groundwater vulnerability of Guyuan City, including DRASTIC model, analytical hierarchy process-DRASTIC model (AHP-DRASTIC) and variable weight theory-DRASTIC model (VW-DRASTIC). The groundwater vulnerability index (GVI) of the study area was calculated in ArcGIS. Based on the magnitude of GVI, the groundwater vulnerability was classified into five classes: very high, high, medium, low, and very low using the natural breakpoint method, and the groundwater vulnerability map (GVM) of the study area was drawn. In order to validate the accuracy of groundwater vulnerability, the Spearman correlation coefficient was used, and the results showed that the VW-DRASTIC model performed best among the three models (ρ=0.83). The improved VW-DRASTIC model shows that the variable weight model effectively improves the accuracy of the DRASTIC model, which is more suitable for the study area. Finally, based on the results of GVM combined with the distribution of F- and urban development planning, suggestions were proposed for further sustainable groundwater management. This study provides a scientific basis for groundwater management in Guyuan City, which can be an example for similar areas, particularly in arid and semi-arid areas.

In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultural pollutants. Groundwater management requires a comprehensive analysis of both horizontal and vertical pollution in coastal aquifers. In this paper, Intrinsic Aquifer Vulnerability (IAV) was assessed on an integrated scale using two classic IAV models (DRASTIC and GALDIT) separately based on a GIS database. Hydrogeological parameters from two classic IAV models were clustered using affinity propagation (AP) clustering algorithm, and silhouette coefficients were used to determine the optimal classification result. In our application, the objects of the AP algorithm are 3320 units divided from the whole study area with 500 m*500 m precision. A comparison of all four outputs in AP-DRASTIC shows that the clustering results of the 4-classification yielded the best silhouette coefficient of 0.406 out of all four. Cluster 4, which comprises 21% of the area, had relatively low level of groundwater contamination, despite its high level of vulnerability as indicated by the classic DRASTIC index. In the second level of vulnerability Cluster 3, 53.8% of all water samples were found to be contaminated, indicating a greater level of nitrate contamination. With respect to AP-GALDIT, the silhouette coefficient for result 7-classification reaches the highest value of 0.343. There was a high level of vulnerability identified in Clusters 2, 4 and 5 (34.7% of the study area) relating to the classic GALDIT index. The concentration of chloride in all water samples obtained in these areas was extremely high. Groundwater management should be addressed by AP-DRASTIC results on anthropogenic activity/contamination control, and by AP-GALDIT results on groundwater extraction limitation. Overall, this method allows for the evaluation of IAV in other coastal areas on an integrated scale, facilitating the development of groundwater management strategies based on a better understanding of the aquifer\'s essential characteristics.

Groundwater contamination risk assessment is an important basis for the protection of groundwater resources and the prevention and control of groundwater pollution. Its evaluation system is usually composed of three elements, which is vulnerability, pollutant source load and groundwater value. The pollutant source load plays an important role in risk assessment. Because of the difference among methods for pollutant sources load quantification, there will inevitably be different demand for the basic information survey on pollutant sources, as well as unknown impact on the final assessment results. In order to explore the impact of quantitative methods for pollutant sources load on groundwater contamination risk assessment, a case study was conducted in the mesoscale region of alluvial fan in Hutuo River, China. The two representative methods of grading index and quantitative index assessment systems were applied to quantify the pollutant sources load, in combination with the same vulnerability and groundwater value. The results constructed from different risk assessment systems were compared. The results showed that there were great differences between the two methods of quantification for pollutant sources load, and the result of contamination risk assessment also revealed significant differences in the feature of risk valve and the spatial distribution of the risk levels. The results of contamination risk assessment were strongly influenced by the choice of quantification for pollutant sources load methods. The grading index method was suitable for large scale region with lower precision of basic information and was simple, and the results of assessment had relatively lower reliability. The quantitative index method was suitable for the mesoscale and micro-scale region with higher precision of basic information and was relatively complicated, and the results of assessment had relatively higher reliability. It was shown that the scale effect of evaluation region had an important influence on the choice of methods.

The main objective of this study is to quantify the groundwater contamination risk of Songhua River Basin by applying a novel approach of integrating public datasets, web services and numerical modelling techniques. To our knowledge, this study is the first to establish groundwater risk maps for the entire Songhua River Basin, one of the largest and most contamination-endangered river basins in China. Index-based groundwater risk maps were created with GIS tools at a spatial resolution of 30arc sec by combining the results of groundwater vulnerability and hazard assessment. Groundwater vulnerability was evaluated using the DRASTIC index method based on public datasets at the highest available resolution in combination with numerical groundwater modelling. As a novel approach to overcome data scarcity at large scales, a web mapping service based data query was applied to obtain an inventory for potential hazardous sites within the basin. The groundwater risk assessment demonstrated that <1% of Songhua River Basin is at high or very high contamination risk. These areas were mainly located in the vast plain areas with hotspots particularly in the Changchun metropolitan area. Moreover, groundwater levels and pollution point sources were found to play a significantly larger impact in assessing these areas than originally assumed by the index scheme. Moderate contamination risk was assigned to 27% of the aquifers, predominantly associated with less densely populated agricultural areas. However, the majority of aquifer area in the sparsely populated mountain ranges displayed low groundwater contamination risk. Sensitivity analysis demonstrated that this novel method is valid for regional assessments of groundwater contamination risk. Despite limitations in resolution and input data consistency, the obtained groundwater contamination risk maps will be beneficial for regional and local decision-making processes with regard to groundwater protection measures, particularly if other data availability is limited.