基于WQI和GIS技术的阿坝地下水水质评价

Ogbonnaya Paul Kanu, Ejikeme Ugwoha, Ngozi U. Udeh, Victor Amah
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引用次数: 0

摘要

地下水被认为是城市环境中人类消费和活动最重要的淡水来源。地下水质量评价对于避免地下水污染对人类健康、农业和经济的负面影响至关重要。本研究对尼日利亚阿比亚州(Abia State)的地下水水质进行了评价。在雨季和旱季采集了16个钻孔的32个水样,分析了pH、电导率、总硬度、浊度、温度、BOD5、COD、Pb、Cd、Cr、NH3、TDS、SO4、NO3和PO4。采用加权平均水质指数(WAWQI)法计算水质指数。对比了普通克里格法、经验贝叶斯克里格法(EBK)、逆距离加权法(IDW)和样条插值法(Spline interpolation)等地统计分析方法在ArcGIS变量浓度空间评价中的应用。在旱季,100%的井位水质指数超过100,不适合饮用。在雨季,87.5%的井位WQI值在100以上,不适合饮用,6.25%的井位WQI非常差,6.25%的井位WQI值在26 - 50之间的良好水质范围内。枯水期的平均误差为0.372483,RMSE为0.5515,RMSSE为1.030492;雨季的平均误差为0.05625,RMSE为10,RMSSE为0.986448,EBK插值方法是确定WQI的最佳拟合模型。雨季的WQI值普遍低于旱季的WQI值,这是由于雨季地表水径流的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Groundwater Quality Assessment in Aba (Abia State) Using WQI and GIS Techniques
Groundwater is considered as the most important source of freshwater for human consumption and activities in the urban environment. The assessment of groundwater quality is critical to avoid the negative effects of polluted groundwater on human health, agriculture, and the economy. In this study, the quality of groundwater in Aba (Abia State), Nigeria was assessed. Thirty-two water samples were taken from sixteen boreholes during the rainy and dry seasons, and analysed for pH, Electrical Conductivity, Total Hardness, Turbidity, Temperature, BOD5, COD, Pb, Cd, Cr, NH3, TDS, SO4, NO3 and PO4. The Weighted Average Water Quality Index (WAWQI) approach was used to compute the Water Quality Index (WQI). Geostatistical analysis methods such as Ordinary Kriging, Empirical Bayesian Kriging (EBK), Inverse Distance Weighting (IDW) and Spline interpolation methods were compared in the spatial evaluation of variable concentrations using ArcGIS. In the dry season, 100% of the borehole locations had Water Quality Index values above 100 which is Unsuitable for drinking purpose. For the rainy season, 87.5% of the borehole locations had WQI values above 100 which is Unsuitable for drinking purpose, 6.25% had WQI which was of very poor quality and 6.25% had WQI values within the range of good water quality between 26 and 50. With a mean error of 0.372483, RMSE of 0.5515, and RMSSE of 1.030492 for the dry season and a mean error of 0.05625, RMSE of 10, and RMSSE of 0.986448 for the rainy season, the EBK interpolation approach was the best fit model for the WQI determination. WQI values for rainy season were generally lower than the WQI values of the dry season, attributable to surface water run-off during the rains.
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