当前位置: X-MOL 学术Appl. Water Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Integrating hydrogeological and second-order geo-electric indices in groundwater vulnerability mapping: A case study of alluvial environments
Applied Water Science ( IF 5.5 ) Pub Date : 2021-06-25 , DOI: 10.1007/s13201-021-01437-x
Nyakno Jimmy George 1
Affiliation  

AVI (Aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology and depth to the aquifer), GLSI (geo-electric layer susceptibility indexing) and S (longitudinal unit conductance) models were used to assess economically exploitable groundwater resource in the coastal environment of Akwa Ibom State, southern Nigeria. The models were employed in order to delineate groundwater into its category of vulnerability to contamination sources using the first- and second-order geo-electric indices as well as hydrogeological inputs. Vertical electrical sounding technique employing Schlumberger electrode configuration was carried out in 16 locations, close to logged boreholes with known aquifer core samples. Primary or first-order geo-electric indices (resistivity, thickness and depth) measured were used to determine S. The estimated aquifer hydraulic conductivity, K, calculated from grain size diameter and water resistivity values were used to calculate hydraulic resistance (C) used to estimate AVI. With the indices assigned to geo-electric parameters on the basis of their influences, GOD and FSLI were calculated using appropriate equations. The geologic sequence in the study area consists of geo-electric layers ranging from motley topsoil, argillites (clayey to fine sands) and arenites (medium to gravelly sands). Geo-electric parametric indices of aquifer overlying layers across the survey area were utilized to weigh the vulnerability of the underlying water-bearing resource to the contaminations from surface and near-surface, using vulnerability maps created. Geo-electrically derived model maps reflecting AVI, BOD, FLSI and S were compared to assess their conformity to the degree of predictability of groundwater vulnerability. The AVI model map shows range of values of log C ( −3.46—0.07) generally less than unity and hence indicating high vulnerability. GOD model tomographic map displays a range of 0.1–0.3, indicating that the aquifer with depth range of 20.5 to 113.1 m or mean depth of 72. 3 m is lowly susceptible to surface and near-surface impurities. Again, the FLSI map displays a range of FLSI index of 1.25 to 2.75, alluding that the aquifer underlying the protective layer has a low to moderate vulnerability. The S model has values ranging from 0.013 to 0.991S. As the map indicates, a fractional portion of the aquifer at the western (Ikot Abasi) part of the study area has moderate to good protection (moderate vulnerability) while weak to poor aquifer protection (high vulnerability) has poor protection. The S model in this analysis seems to overstate the degree of susceptibility to contamination than the AVI, GOD and GLSI models. From the models, the categorization of severity of aquifer vulnerability to contaminations is relatively location-dependent and can be assessed through the model tomographic maps generated.



中文翻译:

在地下水脆弱性绘图中整合水文地质和二阶地电指数:冲积环境案例研究

AVI(含水层脆弱性指数)、GOD(地下水发生、上覆岩性和含水层深度)、GLSI(地电层敏感性指数)和 S(纵向单位电导)模型用于评估沿海环境中可经济开采的地下水资源尼日利亚南部阿夸伊博姆州。使用这些模型是为了使用一阶和二阶地电指数以及水文地质输入将地下水划分为对污染源的脆弱性类别。采用斯伦贝谢电极配置的垂直电测深技术在 16 个位置进行,靠近具有已知含水层岩心样本的测井钻孔。测量的初级或一阶地电指数(电阻率、厚度和深度)用于确定 S。根据颗粒直径和水电阻率值计算出的估计含水层水力传导率 K 用于计算用于估计 AVI 的水力阻力 (C)。使用根据其影响分配给地电参数的指数,使用适当的方程计算 GOD 和 FSLI。研究区的地质层序由地电层组成,范围从杂色表土、泥质岩(粘土到细砂)和砂岩(中砂到砾砂)。使用创建的脆弱性地图,利用整个调查区含水层上覆层的地电参数指数来衡量底层含水资源对地表和近地表污染的脆弱性。反映 AVI、BOD、比较 FLSI 和 S 以评估它们与地下水脆弱性的可预测程度的一致性。AVI 模型图显示 log C (-3.46-0.07) 的值范围通常小于 1,因此表示高度脆弱性。GOD模型断层图显示范围为0.1-0.3,表明深度范围为20.5-113.1 m或平均深度为72. 3 m的含水层对地表和近地表杂质的敏感性较低。同样,FLSI 地图显示的 FLSI 指数范围为 1.25 到 2.75,暗示保护层下方的含水层具有低到中等的脆弱性。S 模型的值范围从 0.013 到 0.991S。正如地图所示,研究区西部(Ikot Abasi)含水层的一小部分具有中等至良好的保护(中等脆弱性),而含水层保护较弱至较差(高度脆弱性)的保护较差。本分析中的 S 模型似乎比 AVI、GOD 和 GLSI 模型夸大了对污染的敏感性程度。从模型中,含水层易受污染的严重程度的分类相对取决于位置,可以通过生成的模型断层扫描图进行评估。

更新日期:2021-06-25
down
wechat
bug