Comprehensive health risk analysis of heavy metal pollution using water quality indices and Monte Carlo simulation in R software.

Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr). During the wet and dry seasons, 25% and 40% of the regions exhibited high CD, respectively. According to non-carcinogenic risk assessment, Cr presents the highest CDI and HQ in children and adults, followed by Mn, As and HI values, indicating elevated risk for children. The highest carcinogenic risk was for Cr in adults, while the lowest was for Cd in children. The sensitivity analysis found that heavy metal concentration and ingestion rate significantly impact both carcinogenic and non-carcinogenic risks. These findings provide critical insights for shaping policy and allocating resources towards effectively managing heavy metal contamination in residential drinking water.

An ontology-based study on water quality: probabilistic risk assessment of exposure to fluoride and nitrate in Shiraz drinking water, Iran using fuzzy multi-criteria group decision-making models.

World Health Organization reports that 2.2 million people die yearly from insufficient sanitary drinking water. This ontology-based study focused on investigating the chemical quality of drinking water through a new water quality index designed by fuzzy multi-criteria group decision-making methods, merged with GIS, and, secondly, surveying non-carcinogenic risk assessment of fluoride and nitrate using Monte Carlo simulation and sensitivity analysis in Shiraz's water sources. F-, NO3-, NO2-, EC, TDS, alkalinity, TH, SO42-, Cl-, and Na were applied in the WQI. The NO3- mean concentrations were 23.15 and 27.66 mg/L in the cold and warm seasons, while the mean concentrations of fluoride were 0.50 and 0.46 mg/L during the cold and warm period. The 95th centiles of fluoride's HQs among infants, children, teenagers, and adults were 0.56, 0.7, 0.49, and 0.4, respectively, in the cold season, which was 0.65 and 0.81, respectively, 0.57 and 0.46 for mentioned groups in the warm season. In comparison, the 95th centiles of nitrate's HQs among infants, children, teenagers, and adults were 1.27, 1.59, 1.13, and 0.9, respectively. The HQs were more than 1 for infants, children, and teenagers, so nitrate can have various adverse effects, whereas fluoride does not adversely affect all aging groups in both seasons. Also, nitrate concentration can increase the non-carcinogenic risk, which the IR and ED lead to the HQ increasing. In contrast, BW has a negative effect on risk increasing. Overall, source management of these parameters can significantly reduce the concentration of nitrate and their adverse human health effect.