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Shah SSA, Asif AR, Ilahi M, Haroon H, Islam I, Qadir A, Nisar I, Sani MMU, Iqbal R, Rahman MHU, Arslan M, Alwahibi MS, Elshikh MS, Ditta A. Geographical distribution of radon and associated health risks in drinking water samples collected from the Mulazai area of Peshawar, Pakistan. Sci Rep 2024; 14:6042. [PMID: 38472226 PMCID: PMC10933375 DOI: 10.1038/s41598-024-55017-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Geospatial methods, such as GIS and remote sensing, map radon levels, pinpoint high-risk areas and connect geological traits to radon presence. These findings direct health planning, focusing tests, mitigation, and policies where radon levels are high. Overall, geospatial analyses offer vital insights, shaping interventions and policies to reduce health risks from radon exposure. There is a formidable threat to human well-being posed by the naturally occurring carcinogenic radon (222Rn) gas due to high solubility in water. Under the current scenario, it is crucial to assess the extent of 222Rn pollution in our drinking water sources across various regions and thoroughly investigate the potential health hazards it poses. In this regard, the present study was conducted to investigate the concentration of 222Rn in groundwater samples collected from handpumps and wells and to estimate health risks associated with the consumption of 222Rn-contaminated water. For this purpose, groundwater samples (n = 30) were collected from handpumps, and wells located in the Mulazai area, District Peshawar. The RAD7 radon detector was used as per international standards to assess the concentration of 222Rn in the collected water samples. The results unveiled that the levels of 222Rn in the collected samples exceeded the acceptable thresholds set by the US Environmental Protection Agency (US-EPA) of 11.1 Bq L-1. Nevertheless, it was determined that the average annual dose was below the recommended limit of 0.1 mSv per year, as advised by both the European Union Council and the World Health Organization. In order to avoid the harmful effects of such excessive 222Rn concentrations on human health, proper ventilation and storage of water in storage reservoirs for a long time before use is recommended to lower the 222Rn concentration.
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Affiliation(s)
- Syed Samran Ali Shah
- School of Computing and Engineering, University of West London, Ealing, London, UK
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
| | - Abdul Rahim Asif
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
| | - Manzoor Ilahi
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
- GIS & Space Applications in Geosciences (G-SAG) Lab, National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
| | - Haseeb Haroon
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
| | - Ihtisham Islam
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
- Department of Geology, Shaheed Benazir Bhutto University Sheringal, Dir (U), 18000, Pakistan
| | - Adnan Qadir
- Pakistan Museum of Natural History, Shakarparian National Park, Garden Ave, Islamabad, 44000, Pakistan
| | - Irfan Nisar
- National Centre of Excellence in Geology, University of Peshawar, Peshawar, 25120, Pakistan
| | | | - Rashid Iqbal
- Department of Agronomy, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Muhammed Habib Ur Rahman
- Agroecology and Organic Farming Group, Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
- Crop Science, INRES, University of Bonn, Germany, Bonn, Germany
| | - Muhammad Arslan
- Agroecology and Organic Farming Group, Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany.
| | - Mona S Alwahibi
- Department of Botany and Microbiology, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Mohamed S Elshikh
- Department of Botany and Microbiology, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Allah Ditta
- Department of Environmental Sciences, Shaheed Benazir Bhutto University Sheringal, Dir (U), 18000, Pakistan.
- School of Biological Sciences, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia.
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Fallatah O, Ahmed M, Gyawali B, Alhawsawi A. Factors controlling groundwater radioactivity in arid environments: An automated machine learning approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 830:154707. [PMID: 35331768 DOI: 10.1016/j.scitotenv.2022.154707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/02/2022] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
Groundwater resources in the Kingdom of Saudi Arabia (KSA) have high levels of natural radioactivity. Within the northwestern KSA, gross alpha (α) and gross beta (β) levels exceed national and international drinking-water limits. In this study, we developed and used an automated machine learning (AML) approach to quantify relationships between gross α and gross β activities and different geological, hydrogeological, and geochemical conditions. Two AML model groups (group I for gross α; group II for gross β) were constructed, using water samples collected from 360 irrigation and water supply wells, to define a robust model that explains the spatial variability in gross α and gross β activities, as well as variables that control the gross activities. Each group contained four model families: deep neural network (DNN), gradient boosting machine (GBM), generalized linear model (GLM), and distributed random forest (DRF). Model inputs include chemical compositions as well as geological and hydrogeological conditions. Three performance metrics were used to evaluate the models during training and testing: normalized root mean square error (NRMSE), Pearson's correlation coefficient (r), and Nash-Sutcliff efficiency (NSE) coefficient. Results indicate that (1) the GBM model outperformed (training: NRMSE: 0.37 ± 0.10; r: 0.92 ± 0.05; NSE: 0.85 ± 0.09; testing: NRMSE: 0.71 ± 0.08; r: 0.72 ± 0.08; NSE: 0.49 ± 0.12) the DNN, DRF, and GLM models when modelling gross α activities; (2) gross α activities are controlled by pH, stream density, nitrate, manganese, and vegetation index; (3) the DRF model outperformed (training: NRMSE: 0.41 ± 0.05; r: 0.92 ± 0.02; NSE: 0.83 ± 0.04; testing: NRMSE: 0.67 ± 0.09; r: 0.77 ± 0.07; NSE: 0.54 ± 0.12) the GBM, DNN, and GLM models when modelling gross β activities; (4) input variables that affect the gross β actives are pH, temperature, stream density, lithology, and nitrate; and (5) no single model could be used to model both gross α and gross β activities-instead, a combination of AML models should be used. Our computationally efficient approach provides a framework and insights for using AML techniques in water quality investigations and promotes more and improved use of different geological, hydrogeological, and geochemical datasets by the scientific community and decision makers to develop guidelines for mitigation.
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Affiliation(s)
- Othman Fallatah
- Department of Nuclear Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
| | - Mohamed Ahmed
- Department of Physical and Environmental Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA.
| | - Bimal Gyawali
- Department of Physical and Environmental Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA
| | - Abdulsalam Alhawsawi
- Department of Nuclear Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia
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