鲁西北平原区浅层地下水质量评价及指标优化

Quality evaluation and index optimization of shallow groundwater in the Northwest Plain of Shandong Province

【目的】为优化地下水水质监测方案,确定鲁西北平原地下水水质的主要控制因素,快速掌握地下水水质变化,【方法】基于2020年鲁西北平原地区浅层地下水水质数据,利用统计学软件、GIS软件对研究区水质情况进行描述性统计与空间信息差异化分析;基于熵权水质指数(EWQI)利用多元线性回归模型探讨能代表研究区地下水水质的化学指标。【结果】结果显示:鲁西北平原地下水水质趋于一个较为中等的状态,冲积海积平原咸水水文地质亚区与冲积平原咸淡水水文地质亚区水质较差,冲积洪积平原淡水水文地质亚区水质相对较好;EWQI模型指标优化后代表整个研究区地下水水质的水化学指标为TDS、Mn^(2+)、I^(-)、F^(-)和Al^(3+),指标优化后EWQI_(min)模型R~2和RMSE值分别为0.996和5.094,百分比误差PE为3.615%。【结论】结果表明:EWQI_(min)模型优选指标具有较好的代表性,可用作水质监测主要监控对象,为水质监测网优化调整及相关管理措施提供了参考。

[Objective]In order to optimize the groundwater quality monitoring plan,identify the primary controlling factors of groundwater quality in the Northwest Shandong Plain,and rapidly assess variations in groundwater quality,[Methods]We based on the shallow groundwater quality data from the Northwest Shandong Plain in 2020,statistical software and GIS software were used to conduct descriptive statistics and spatial information differential analysis of the water quality in the study area.The Entropy Weighted Quality Index(EWQI) was used,and a multiple linear regression model was employed to explore the hydrochemical indicators that represent groundwater quality in the study area.[Results]The result indicated that the groundwater quality in the Northwest Shandong Plain tends to be in a moderate state.The water quality in the alluvial and aeolian sediment plain hydrogeological sub-areas and the alluvial plain brackish-freshwater hydrogeological sub-areas is relatively poor,while the water quality in the alluvial floodplain freshwater hydrogeological sub-area is relatively better.After optimizing the EWQI model indicators,the hydrochemical indicators that represent the groundwater quality in the entire study area are TDS,Mn~(2+),I~-,F~-,and Al~(3+).The R~2 and RMSE values of the optimized EWQI_(min) model are 0.996 and 5.094,respectively,with a percentage error(PE) of 3.615%.[Conclusion]The result demonstrate that the selected indicators in the EWQI_(min) model exhibit good representativeness and can be used as the primary monitoring targets for water quality assessment.This model provides valuable references for optimizing and adjusting the water quality monitoring network and implementing relevant management measures.