Comparison of stochastic and traditional water quality indices for mapping groundwater quality zones
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ORIGINAL ARTICLE
Comparison of stochastic and traditional water quality indices for mapping groundwater quality zones Khalid Mahmood1 · Rida Batool2 Received: 10 March 2020 / Accepted: 21 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This study has compared the use of standard water quality overlay techniques with that of Getis-Ord Gi* statistical techniques for visualizing the spatial distribution of water quality parameters in an unconfined aquifer beneath the city of Lahore in Pakistan. The non-uniformly distributed groundwater sampling points were uniformly transformed to a grid of evenly distributed values to apply Getis-Ord Gi* statistics. An optimized neighborhood distance value of 700 m was determined for the Getis-Ord Gi* assessment, showing that the aquifer is regionally continuous and there are no barriers to lateral groundwater flow. This statistical approach was initially applied to individual parameters and was found to better define hotspots than that of the conventional method. Similarly, the use of Getis-Ord Gi* values improved the assessment of hotspots of water quality index (WQI) values than conventional overlay techniques. Keywords Groundwater quality · Geographic information system · Spatial interpolation · Getis-ord Gi* · Hotspot analysis · Water quality indices
Introduction Groundwater is a key resource which, under suitable hydrogeological conditions, is important for water supply and sustainability of certain ecosystems. However, the provision of safe sources of drinking water to rapidly increasing populations is becoming increasingly difficult which are further severely affected by pollution caused by land-use activities (Caizhi et al. 2016; Mahmood and Batool 2019). The effects of climate change, over-exploitation of groundwater to meet needs of rapidly increasing populations, urban sprawl with predominantly impervious surfaces and modern living standards are all factors that are limiting the availability of sustainable groundwater for use (Abbas et al. 2015). Consequently, there has been an increased focus by policy makers on conserving groundwater quality. To get an insight into the quality of underlying aquifer, samples from * Khalid Mahmood [email protected]; [email protected] 1
Remote Sensing, GIS and Climatic Research Lab (National Center for GIS and Space Applications), Department of Space Science, University of the Punjab, Lahore, Pakistan
Department of Space Science, University of the Punjab, Lahore, Pakistan
2
boreholes provide the basic data needed (Jang et al. 2017). Increasingly, these field-based investigations are being supplemented by the use of geostatistical and geospatial algorithms to determine how groundwater interacts with the environment. Although interpretation of groundwater quality of a region is important, it is equally important to determine the spatial distribution and influence of groundwater quality variations in an aquifer (Mahmood et al. 2016). This is typically carried out using geographic informa
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