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Identifying Major Factors Controlling Groundwater Quality in Semiarid Area Using Advanced Statistical Techniques

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  • M. Salahat
  • M. Al-Qinna
  • K. Mashal
  • N. Hammouri

Abstract

There are many factors controlling groundwater pollution and vulnerability. However, the factors’ weights are still not reasonably investigated. In order to assess groundwater quality and the controlling factors in semiarid region, 178 groundwater samples were collected and analyzed for salinity and nitrate content. New statistical techniques of prediction profiler and hierarchical cluster combined with geographic information systems (GIS) were used to assess the groundwater quality based on three categorical controlling factors; landuse/ land cover (LULC), soil texture, and aquifer type. It is hypothesized these factors are controlling groundwater quality with various weights. Groundwater salinity across the study area varied from 327.0 to 9110.0 mg/L, while nitrate ranged from 0.2 mg/L to 339.6 mg/L. Both prediction profiler and cluster analyses provided excellent tools for quantifying the pollution magnitudes, weighing the controlling factors, and visualizing the pollution zones. Prediction profiler showed high capability to predict groundwater pollution (P > 0.0001 and 0.0038 for salinity and nitrate, respectively) where LULC was the most effective factor, followed by aquifer type and soil texture class. According to desirability function analysis, maximum salinity and nitrate pollution was predicted to be associated with irrigated agriculture lands at shallow aquifers with silty clay loam soils. Hierarchical cluster analysis combined with GIS mapping was able to group the controlling factors into six vulnerability zones. The generated groundwater spatial pollution map allowed for potential pollution sources identification (e.g. fertilizer use, treated waste water, overdrafting). This paper also offers detailed mapping for decision makers to allow further ecosystem restoration and rehabilitation planning. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • M. Salahat & M. Al-Qinna & K. Mashal & N. Hammouri, 2014. "Identifying Major Factors Controlling Groundwater Quality in Semiarid Area Using Advanced Statistical Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3829-3841, September.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:11:p:3829-3841
    DOI: 10.1007/s11269-014-0712-1
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    References listed on IDEAS

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    1. Vahid Gholami & Zabihollah Yousefi & Hosseinali Zabardast Rostami, 2010. "Modeling of Ground Water Salinity on the Caspian Southern Coasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(7), pages 1415-1424, May.
    2. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    3. Insaf Babiker & Mohamed Mohamed & Tetsuya Hiyama, 2007. "Assessing groundwater quality using GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(4), pages 699-715, April.
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    1. Esmaeil Asadi & Mohammad Isazadeh & Saeed Samadianfard & Mohammad Firuz Ramli & Amir Mosavi & Narjes Nabipour & Shahaboddin Shamshirband & Eva Hajnal & Kwok-Wing Chau, 2019. "Groundwater Quality Assessment for Sustainable Drinking and Irrigation," Sustainability, MDPI, vol. 12(1), pages 1-13, December.
    2. Chandrashekhar Bhuiyan & Prashant Kumar Champati Ray, 2017. "Groundwater Quality Zoning in the Perspective of Health Hazards," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 251-267, January.
    3. Faisal Aburub & Wa’el Hadi, 2018. "A New Associative Classification Algorithm for Predicting Groundwater Locations," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-26, December.

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