Abstract：
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.
摘要：
地下水硝酸盐污染已成为全球紧迫的问题。鉴于其可能对含水层产生长期影响，保护措施应主要集中在预防上。借鉴地下水脆弱性(GV)理论，原始DRASTIC模型和与人类活动相关的参数被用作输入，并与LightGBM回归算法集成，以促进硝酸盐指数（NI）预测任务。进行SHAP分析以有效地检查参数对NI预测的贡献并解释参数相互作用的问题。此外,为了减轻内在GV模型的局限性，通过将DRASTIC指数与NI线性组合，得出了复合硝酸盐指数（CNI）。本研究提出的框架为不同时间段的地下水资源管理提供了适应性策略。干旱和半干旱气候的代表性地区，银川地区,使用该框架进行了研究。与2012年相比，2022年内在GV指数发生了空间变化。人类活动增加了硝酸盐浓度的影响，如DRASTIC指数和硝酸盐浓度之间的皮尔逊相关系数-0.082所示。NI预测污染水平会显著增加，范围从-0.116到0.968。根据SHAP分析,2022年NI水平显著上升主要是由于高价值工农业生产。2022年，12.02%的地区在CNI中至少增加了0.549。42.1％的地区被归类为中等或高CNI水平。该农场被确定为硝酸盐污染的重要来源。非城市地区的小规模农业和畜牧业活动也加剧了地下水污染。需要在高增长和高水平的CNI地区实施动态地下水管理策略。
