Multi-criterion approach for assessment of hydrochemical characteristics and human health risk of groundwater in Khartoum State, Sudan

Mohammed, Musaab A. A.; Eltijani, Abdelrhim; Szabó, Norbert Péter; Szűcs, Péter

doi:10.21203/rs.3.rs-2481210/v1

Cited by 3 publications

(1 citation statement)

References 61 publications

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“…The western parts of the study area are associated with low values of SAR, likely due to the influence of groundwater recharge on groundwater samples. The highest values in the southern and central parts are generally due to the high mineralization of groundwater samples due to the dissolution of halite minerals within the Nubian formations (Abdelsalam et al, 2016;Mohammed et al, 2023b). The values of predicted SAR recorded in the KNN model ranged from 0.71 to 7.98, while in the SVR model they ranged from 0.61 to 9.40.…”

Section: Spatial Prediction Of Irrigation Indicesmentioning

confidence: 96%

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Mohammed,

Kaya,

Mohamed

et al. 2023

Front. Earth Sci.

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Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

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Section: Spatial Prediction Of Irrigation Indicesmentioning

confidence: 96%

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Mohammed,

Kaya,

Mohamed

et al. 2023

Front. Earth Sci.

Self Cite

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Characterization of groundwater aquifers using hydrogeophysical and hydrogeochemical methods in the eastern Nile River area, Khartoum State, Sudan

2023

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The primary goals of this research are to detect the spatial variation of the hydrogeological characteristics and evaluate the groundwater quality in the eastern Nile River, Khartoum state, Sudan, using geophysical and hydrochemical methods. Thirteen Vertical electrical soundings (VES), using Schlumberger configuration, were measured along three profiles to characterize the groundwater aquifer. VES findings denoted that the study area comprises two hydraulically connected aquifers. The upper aquifer of sand has an average thickness of 50 m, and the lower aquifer is composed of sandstone of a thickness of up to 300 m. The results of VES inversion were further used to measure aquifer characteristics, including transverse resistance, longitudinal conductance, hydraulic conductivity, and transmissivity. The detected average values of these parameters were 6690 Ωm2, 1.4 Ω−1, 264 m2/d and 4 m/day, respectively. In addition, regression analysis was performed to suggest local relationships for estimating aquifer characteristics within the study area. On the other hand, total longitudinal conductance was used to predict the protective strength of the hydrogeological columns, ranging from 1.7 to 5.8 Ω−1; as a result, the protective capacity of the aquifer ranged from good to very good, suggesting potable water quality. This result was subsequently confirmed by the groundwater quality index (GWQI) model. Eleven physiochemical parameters analyzed for nine boreholes were used in GWQI estimation to assess groundwater quality in the study area. The primary analysis of the hydrochemical parameters indicated that almost all parameters are below permissible limits prescribed by the World Health Organization (WHO). The computed GWQI varies between 34.8 and 148, and the majority of groundwater samples, precisely 55.5%, are good water types, while 22.2% of the samples are in an excellent quality state. This research concluded that the groundwater aquifer in the study area is ideal for groundwater exploitation. However, applying a detailed geophysical and hydrochemical survey is recommended to reduce the uncertainty of the resulting models.

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Hydro-chemometrics of the Nubian Aquifer in Sudan: an integration of groundwater quality index, multivariate statistics, and human health risk assessment

Mohammed,

Eltijani,

Szabó

et al. 2023

Discov Water

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This research employed the groundwater quality index (GWQI), multivariate statistical methods, and human health risk assessment model to investigate the suitability of groundwater for domestic uses in northern Khartoum state, Sudan. The groundwater samples were analyzed for eleven physiochemical parameters, including pH, EC, TDS, TH, Cl−, SO42−, NO3−, Ca2+, Mg2+, Na+, HCO3− and the primary investigation indicated the deviation of these parameters from World Health Organization (WHO) standards. The hydrochemical analysis revealed different groundwater facies with the dominance of Ca–Mg–HCO3 water type. Consequently, the groundwater samples were classified, based on GWQI, into three categories as 76.4% of the samples fall in the excellent water class, 17.6% are projected in the good water class, and 5.9% of groundwater samples are considered unsuitable for human consumption. The multivariate statistical methods were applied, including Pearson’s correlation analysis, principal component analyses (PCA), and hierarchical cluster analysis (HCA). Three principal components (PCs) explaining 86.07% of total variances are extracted. These PCs indicated that rock-water interactions and agricultural practices influence groundwater quality in the study area. Additionally, HCA is used to categorize groundwater samples based on the concentration of the physiochemical parameters. Consequently, three types of groundwater were identified as low, medium, and highly mineralized. In the final stage, the non-carcinogenic human health risk was assessed based on the concentration of NO3− using the United States Environmental Protection Agency (USEPA) models. The obtained hazard quotient for children indicated that 64.7% of groundwater samples are beyond the permissible limit (1 <), and the use of these samples may result in health consequences. Therefore, remedial measures are suggested for the sustainable use of groundwater.

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Multi-criterion approach for assessment of hydrochemical characteristics and human health risk of groundwater in Khartoum State, Sudan

Cited by 3 publications

References 61 publications

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices

Characterization of groundwater aquifers using hydrogeophysical and hydrogeochemical methods in the eastern Nile River area, Khartoum State, Sudan

Hydro-chemometrics of the Nubian Aquifer in Sudan: an integration of groundwater quality index, multivariate statistics, and human health risk assessment

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