Stochastic health risk profiling of potentially toxic elements in Iranian ornamental construction paints: assessing and Monte Carlo simulation

Paints consist of intricate combinations of solvents, additives, and pigments that provide the desired color, coverage, and durability, and pose a human health risk due to potentially toxic elements (PTEs), including lead (Pb), chromium (Cr), and cadmium (Cd), which accumulate in biological systems. This research innovatively assessed the non-carcinogenic and carcinogenic health risks posed by PTEs in Iranian decorative (ornamental) paints and emphasized the need for awareness raising and the development of control regulations. The PTEs concentrations were determined through wet acid digestion and analyzed through ICP-OES. The findings indicated that Pb concentrations ranged from 689.4 to 858.6 mg/kg, Cr concentrations from 698 to 946.4 mg/kg, and Cd concentrations between 0.24 and 0.37 mg/kg, revealing that Pb and Cr values exceeded the permissible limits. The findings suggest that children exhibit a heightened susceptibility to these pollutants due to their unique behaviors and physiological traits. The ingestion route represented the primary contribution to the total hazard quotient, accounting for 96.8% in children and 58.4% in adults. The adults' hazard index (HI) for Pb and Cd was below the safe threshold of 1, whereas Cr surpassed this limit concerning non-carcinogenic risk. In children, the HI for both Pb and Cr surpassed the acceptable limit. Total Lifetime cancer risk (TLCR) values for both groups in Cr were higher than the acceptable range established by the USEPA, with relatively higher values observed in children. Among the three metals analyzed, Cr exhibited the most significant potential health risk, followed by Pb and Cd. Ingestion was identified as the primary route of exposure, while inhalation and dermal routes were less significant. To enhance the accuracy of exposure risk assessments for PTEs, a Monte Carlo simulation was utilized as a probabilistic algorithm to minimize uncertainties.