The hydrogeochemical characteristics, water quality and health risk statuses of waters in Umunya district, southeastern Nigeria were studied, in attempt to evaluate their suitability for drinking and domestic purposes. Twelve groundwater and 3 surface water samples were analyzed for 26 physicochemical and hydrogeochemical parameters, using standard techniques. Results show that dominance of cations and anions is in the order Ca²⁺ > Na⁺ > K⁺ > Mg²⁺ and HCO₃^– > Cl^– > NO₃^– > SO₄^–, respectively. Order of dominance of the heavy metals is Pb > Zn > Fe > Ni > Mn > Cr > Ba. Eight water types were identified, with Ca–Na–HCO₃ (26.66%) and Na–Cl–HCO₃ (20%) dominating the study area. All the water types characterize five major facies. Further, the result revealed that the physical properties and chemical ionic concentrations in the waters are well below standard maximum permissible limits, although majority of the samples have pH values off the allowable limits of 6.5–8.5, classing the waters as slightly acidic. Generally, the water quality in the study area is deteriorated due to the presence of high levels of heavy metals. Water quality index results show that 46.67% of the water samples are in excellent and good categories. 13.33% are in poor water category, whereas 40% are in category unsuitable for drinking purposes. A good percentage of the waters predispose users to health risks. Stoichiometric and statistical analyses revealed that the variations in chemistry and quality of the waters are due to combined influence of human activities and geogenic processes (silicate weathering and ionic exchanges). Treatment of contaminated waters before use is, therefore, recommended.

This paper assessed the degree of heavy metals pollution, the possible pollution sources, and the human health risks of groundwater from shallow Eocene Aquifer in selected urban centers of Onitsha, Nigeria. The nitrate pollution was low in all of the groundwater samples. However, most of the heavy metals exceeded their maximum allowable limits. The heavy metal pollution index and contamination index, respectively, revealed that 45.83 and 70.83% of the total samples are unsuitable for drinking. However, both the water quality index and synthetic pollution index revealed that 91.67% of the samples are unfit for consumption. Based on the ecological risk index, 58.33% of the samples pose a very high ecological risk. The order of the heavy metals’ impacts as revealed by the calculated health hazard quotients was Pb > Cd > Ni > Cu > Mn > Fe > Cr (for children) and Pb > Cd > Ni > Mn > Cu > Fe > Cr (for adults). Hazard index values ranged from 0.438 to 46.367 for children and 0.127–20.294 for adults. In this study, 91.67% of the samples pose a medium to high chronic risk due to ingestion by both children and adult populations. However, it is predicted that 62.5, 66.67, 20.83 and 66.67% of the samples, respectively, pose high cancer risks due to Ni, Pb, Cr, and Cd ingestion by children. Meanwhile, 62.5, 37.5, 33.33 and 66.67% of the samples respectively pose high cancer risks due to Ni, Pb, Cr, and Cd ingestion by adults. Multivariate statistical analyses revealed that the release of these heavy metals into the groundwater system is primarily influenced by anthropogenic inputs rather than natural processes.

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.