Machine learning models for water safety enhancement
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 st...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88431-4 |
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| author | Fatemeh Ranjbar Hossein Sadeghi Reza Pourimani Soraya Khanmohammadi |
| author_facet | Fatemeh Ranjbar Hossein Sadeghi Reza Pourimani Soraya Khanmohammadi |
| author_sort | Fatemeh Ranjbar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0da34599db124792a257487b92e4febc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0da34599db124792a257487b92e4febc2025-02-02T12:20:44ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-88431-4Machine learning models for water safety enhancementFatemeh Ranjbar0Hossein Sadeghi1Reza Pourimani2Soraya Khanmohammadi3Department of Physics, Faculty of Sciences, Arak UniversityDepartment of Physics, Faculty of Sciences, Arak UniversityDepartment of Physics, Faculty of Sciences, Arak UniversityFaculty of Industrial and Systems Engineering, Tarbiat Modares UniversityAbstract 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.https://doi.org/10.1038/s41598-025-88431-4Mineral waterWater safetyMachine learningHealth risksRadioactive isotopesPotential cancer risk |
| spellingShingle | Fatemeh Ranjbar Hossein Sadeghi Reza Pourimani Soraya Khanmohammadi Machine learning models for water safety enhancement Scientific Reports Mineral water Water safety Machine learning Health risks Radioactive isotopes Potential cancer risk |
| title | Machine learning models for water safety enhancement |
| title_full | Machine learning models for water safety enhancement |
| title_fullStr | Machine learning models for water safety enhancement |
| title_full_unstemmed | Machine learning models for water safety enhancement |
| title_short | Machine learning models for water safety enhancement |
| title_sort | machine learning models for water safety enhancement |
| topic | Mineral water Water safety Machine learning Health risks Radioactive isotopes Potential cancer risk |
| url | https://doi.org/10.1038/s41598-025-88431-4 |
| work_keys_str_mv | AT fatemehranjbar machinelearningmodelsforwatersafetyenhancement AT hosseinsadeghi machinelearningmodelsforwatersafetyenhancement AT rezapourimani machinelearningmodelsforwatersafetyenhancement AT sorayakhanmohammadi machinelearningmodelsforwatersafetyenhancement |