Machine learning models for water safety enhancement - Fatemeh Ranjbar; Hossein Sadeghi; Reza Pourimani; Soraya Khanmohammadi (Scientific Reports)
Abstract Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially leading to cancer and genetic mutations. A study was conducted using random sampling to assess the concentrations of radioactive isotopes and heavy metals in mineral water from Iran, consumable at Arak City. Notably, specific radiation levels of Ra-226 were not detected, whereas the concentrations of Th-232, K-40, and Cs-137 were found to be below the thresholds established by the World Health Organization (WHO). The annual effective doses derived from the consumption of bottled water were significantly lower than the limits set by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), thereby reducing the risk of cancer. Furthermore, heavy metals such as lead and chromium were not present in the samples, thereby contributing to the overall safety of the water. The Machine Learning (ML) models employed in this study provided accurate predictions, ensuring reliability across various demographic groups and reinforcing the robustness of the findings. Overall, the results suggest that consumable mineral water consumption poses minimal health risks.