干旱区生态环境知识资源中心(Arid): Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt

Arid

DOI	10.3390/w15061216
	Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt
	Gad, Mohamed; Gaagai, Aissam; Eid, Mohamed Hamdy; Szucs, Peter; Hussein, Hend; Elsherbiny, Osama; Elsayed, Salah; Khalifa, Moataz M.; Moghanm, Farahat S.; Moustapha, Moustapha E.; Tolan, Dina A.; Ibrahim, Hekmat
通讯作者	Ibrahim, H
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:6
英文摘要	The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 water facies under the influence of silicate weathering, rock-water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model's thirteen characteristics were extremely important for predicting DWQI. The R-2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R-2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R-2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.
英文关键词	artificial neural network GIS groundwater health index multivariate analysis
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000968350700001
WOS关键词	WATER-QUALITY ; SURFACE-WATER ; TEMPORAL VARIATION ; METAL POLLUTION ; WESTERN DESERT ; RIVER ; INDIA ; CLASSIFICATION ; CONTAMINATION ; PARKINSONS
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399004
推荐引用方式 GB/T 7714	Gad, Mohamed,Gaagai, Aissam,Eid, Mohamed Hamdy,et al. Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt[J],2023,15(6).
APA	Gad, Mohamed.,Gaagai, Aissam.,Eid, Mohamed Hamdy.,Szucs, Peter.,Hussein, Hend.,...&Ibrahim, Hekmat.(2023).Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt.WATER,15(6).
MLA	Gad, Mohamed,et al."Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt".WATER 15.6(2023).