Heavy metals pollution indexing, geospatial and statistical approaches of groundwater within Challawa and Sharada industrial areas, Kano City, North-Western Nigeria

Analysis of heavy metal contamination in groundwater and associated probabilistic human health risk assessment using Monte Carlo simulation: A case study in Gaya, Bihar

The occurrence of heavy metal contamination in groundwater poses significant health risks through ingestion and dermal exposure, with potential links to cancer and other diseases. This study evaluated groundwater samples for 10 heavy metals (Al, As, Cr, Cu, Cd, Fe, Mn, Ni, Pb, and Zn) using ICP-OES. While cadmium and chromium levels were within acceptable limits prescribed by the Bureau of Indian Standards, aluminum and iron exceeded these limits in 56 and 58% of samples, respectively. Other metals surpassed limits in 2-20% of cases. Health risk analysis revealed non-carcinogenic risks for 28% of adults and 44% of children, alongside carcinogenic risks from arsenic (36% of samples) and nickel (46% of samples), especially affecting children. Sensitivity analysis highlighted heavy metal concentration as the key variable influencing risk, and principal component analysis suggested geogenic sources, like rock weathering, as major contributors to contamination. Despite these risks, the heavy metal pollution index remained within acceptable limits for all samples. The study emphasizes the necessity for continuous monitoring and targeted mitigation strategies to address heavy metal contamination and protect public health.

Integration of Water Quality Indices and Multivariate Modeling for Assessing Surface Water Quality in Qaroun Lake, Egypt

Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (Cd), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, Cd, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R2 (0.99) and with R2 (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.