Water sources near mining regions are often susceptible to contamination from toxic elements. This study employs machine learning (ML) techniques to evaluate drinking water quality and identify pollution sources near a chromite mine in Iran. Human health risks were assessed using both deterministic and probabilistic approaches. Findings revealed that concentrations of calcium (Ca), chromium (Cr), lithium (Li), magnesium (Mg), and sodium (Na) in the water samples exceeded international safety standards. The Unweighted Root Mean Square water quality index (RMS-WQI) and Weighted Quadratic Mean (WQM-WQI) categorized all water samples as 'Fair', with average scores of 67.95 and 67.19, respectively. Of the ML models tested, the Extra Trees (ET) algorithm emerged as the top predictor of WQI, with Mg and strontium (Sr) as key variables influencing the scores. Principal component analysis (PCA) identified three distinct clusters of water quality parameters, highlighting influences from both local geology and anthropogenic activities. The highest average hazard quotient (HQ) for Cr was 1.71 for children, 1.27 for adolescents, and 1.05 for adults. Monte Carlo simulation for health risk assessment indicated median hazard index (HI) of 4.48 for children, 3.58 for teenagers, and 2.98 for adults, all exceeding the acceptable threshold of 1. Total carcinogenic risk (TCR) exceeded the EPA's acceptable level for 99.38 % of children, 98.24 % of teenagers, and 100 % of adults, with arsenic (As) and Cr identified as the main contributors. The study highlights the need for urgent mitigation measures, recommending a 99 % reduction in concentrations of key contaminants to lower both carcinogenic and non-carcinogenic risks to acceptable levels.

Water, as the primary human needs, play a vital role in all aspects of our life, such as social environment, production, the economy, food security, and politics. In the present investigation, Heavy Metals (HM) concentrations including Cr, Pb, and Cd, have been ascertained in drinking water resources. Besides, point estimation using Hazard Quotient (HQ) and Hazard Index (HI) formula and sensitivity analysis (SA) using the Monte Carlo Simulation technique, by 10,000 repetitions in Oracle Crystal Ball®, were applied to demonstrate the non-carcinogenic effects of HMs on different exposed populations (Infant, children, teenagers, and adults groups). According to the results, the concentrations of Cr, Pb, and Cd in water resources ranged from 2.2 to 173.7 µg/L, 0.5 to 10 µg/L, and 0.2 to 0.9 µg/L, respectively. The results also showed that HI values were in the order of Pb>Cd >Cr. The results of SA, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of studied HMs had the most effect on increasing sensitivity in the four studied exposed groups.

Electrocoagulation is a widely used method for treating leachate since it is cost effective and eco-friendly. In the present study, the electrocoagulation process was employed to remove chemical oxygen demand (COD), NH₄⁺, total dissolved solids (TDS), total suspended solids (TSS), turbidity, and color from landfill leachate. At first, lime was used as a pretreatment, then the Fe/Gr and Ti/PbO₂/steel electrodes were used, and the optimum electrode was selected. Afterwards, the effects of some variables, including pH, current density, temperature, the inter-electrode distance, and the type of electrolyte were investigated. Results showed that COD, NH₄⁺, TSS, TDS, electrical conductivity (EC), turbidity, color, and pH of effluent pretreatment chemical reached 22,371, 385, 884, 21,820 (mg/L), 13.8 (ms/cm³), 1355 (NTU), 8500 (TCU) and 10, respectively (the removal efficiency was 0, 20.37, 32.4, 61.99, 59.18, and 56.6 percent). With the Fe/Gr electrode, the optimal condition was observed as follows: pH of 7.5, current density of 64 mA/cm², inter-electrode distance was equal to 1.5 cm, temperature at 20 °C, and retention time 2–4 h. Overall, the electrocoagulation with the Fe/Gr electrode was a suitable technology for landfill leachate treatment due to its effectiveness for the removal of both COD and NH₄⁺, with advantageous performance indicators.