Search

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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. [Display omitted] • DRASTIC model and anthropogenic parameters were combined to predict nitrate index. • The innovative composite nitrate SGV framework were applied to Yinchuan regions. • SHAP analysis results support an increase in nitrate loading caused by human activity. • Dynamic groundwater management strategies were designed for different levels of CNI. [ABSTRACT FROM AUTHOR]