Natural Radioelements Concentration Analysis and Risk Assessment in Drinking Water of Mining Area Niger State, North Central Nigeria

Natural Radioelements Concentration Analysis and Risk Assessment in Drinking Water of Mining Area Niger State, North Central Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Natural Radioelements Concentration Analysis and Risk Assessment in Drinking Water of Mining Area Niger State, North Central Nigeria Abdullahi Muhammad, Akbar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6889551/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, gamma spectrometry analysis was performed to determine the activity concentration of 40 K, 226 Ra, and 232 Th in four different water sources in three different communities (Shakwata, Eregi, and Pina) in Niger State, north-central Nigeria. Total 20 water samples were analyzed. The activity concentration of 40 K, 226 Ra, and 232 Th in the water ranged from 0.35 ± 0.03 to 33.28 ± 2.97 Bq l⁻¹, 1.32 ± 0.07 l⁻¹ to 3.06 ± 0.18 Bq l⁻¹, and 0.85 ± 0.14 to 2.41 ± 0.15 Bq l⁻¹, respectively. For all five age groups, the annual effective dose from radionuclide intake in water sources across the three communities exceeded recommended limits, except in sample locations SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2-7 years and 7-12 years, where the 232 Th level was found to be below the recommended limit of 1.0 mSv y⁻¹ set by ICRP. Therefore, considering that the annual effective dose exceeds the WHO and ICRP recommended guidelines, the water sources from the mining areas should be restricted to domestic use and not for public consumption except after treatment. Nuclear Physics Gamma-Ray Spectrometry Radiotoxicity Activity Concentration Annual Effective Dose Niger State Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction As the saying goes, water is life. Every living organism on earth requires water for daily life activity and survival, which implies that water is an essential component of the human life cycle. No wonder, in the past two decades, decision makers from different countries across the globe have carefully treated the study of naturally occurring radionuclide materials (NORMs) in the environment and how it interacts with the human population [ 1 ]. Being an important component of life on earth, water must be treated properly, preserved, and distributed accordingly [ 2 ]. The study of natural radioactivity concentration in drinking water and its sources can be traced back to the 1950s and beyond, as evident from the work of Hursh [ 3 ]. This is growing public concern as the sources of naturally occurring radionuclides (not only in water) increase as well as their effects. Some notable sources of naturally occurring radionuclides include 40 K, 226 Ra, 232 Th, and their decay daughter nuclides. These radionuclides are found in different concentrations in most ground formations, such as granite rocks. According to Shabana and Kinsara (2014) [ 4 ], all granitic compositions usually have a higher level of natural radioactivity than rocks with other compositions. On this note, all surface and groundwater are expected to be composed of a higher concentration of one or more of the natural radionuclides depending on the depth to the basement of the aquifer, the nature of the aquifer rocks, the characteristics of the water involved, and the local geology of the area (soil, rock, and clay). These radionuclides can contaminate surface and underground water through rock-water interaction and migration of radionuclides, which in turn exposes the users to radiation doses [ 4 ], [ 5 ]. Furthermore, other sources of radioactivity in water sources span from the mining of mineral resources by humans, nuclear and non-nuclear accidents, testing of nuclear atomic bombs, and use of radioisotopes in nuclear medicine. The areas of study are characterised with verse arable land for farming and a prevailing scattered opencast mining activity, which is a major occupation of the people and this raises many concerns as the safety of the water bodies in the habitat is at risk due to the release of natural radionuclides by the activities of the miners and this reaches the larger population through the food chain. Health risk can arise from the ingestion or inhalation of a small amount of decayed radionuclides in drinking water, which exposes the human internal body to ionizing radiation. Since the number of NORMs present in drinking water is directly linked to the doses of ionizing radiation received by an individual, it is paramount to accurately measure the activity concentration of each NORMs ingested by the human population as well as via domestic uses. This study presents the spectrometry analysis of different gamma-emitting radionuclides using a high-resolution coaxial sodium iodide detector (NaI(TI)). The radionuclide concentrations in 40 K, 226 Ra, and 232 Th in 20 representative water samples collected from scattered dug wells from three communities (Shakwata, Pina, and Eregi) in Niger State, north-central Nigeria, were further checked with the international set limits for natural radionuclides in water sources used for drinking. The findings from this study may serve as a database for future reference. 2. Materials and Methods 2.1 Study Area The research study areas are located in the Shakwata community (Bosso local government area of Niger State), Eregi (Katcha local government area of Niger State), and Pina (Shiroro local government area of Niger State) in North Central Nigeria. Shakwata is a border community to Minna, the capital of Niger State. It is situated along the Minna-Shiroro expressway at latitude 009°39'31.3"N and longitude 006°37'10.5"E. It is 11 km from Minna. In the same vein, Eregi is a border community between Bida and Minna. It is located along 009°22'42.0"N latitude and 006°19'06.8"E longitude. Farming and fishing is the main occupation of the people of this community. It is about 127 km from Minna along the Minna-Bida expressway, and it is bounded in the south by the great River Niger. On the other hand, Pina is a border community between Shiroro and Minna. It is located at latitude 009°43'34.1"N and longitude 006°45'53.6"E. Farming, mineral exploration, and fishing are the major occupations of the people of this community. It is about 74 km from Minna along the Minna-Shiroro expressway. Figure 1 shows the location map and the sample collection points [ 6 ]. Furthermore, the study areas are characterized by hard rocks of Precambrian basement complex rocks, which spans towards the northern edge of the Niger trough. These basement complex rocks are older granites, the migmatites gneiss rocks, and schist belts. At the southern part, the Precambrian rocks forms a layer with the sedimentary rocks (cretaceous in age) and the alluvium soil formation found at the River Niger border and its tributaries [ 6 ]. 2.2 Sample Collection Twenty (20) scattered representative water samples were collected from functional dug wells and rivers in the three areas of study (Shakwata, Pina, and Eregi) at different locations, as seen in Fig. 1 . The sampling locations were selected based on the population density and the usability of the wells. Water samples were collected in the rainy season between May and August 2024, and the most active and frequently used dug wells and boreholes were considered. Before the water sampling, each dug well was allowed to run for 3 minutes before sample collection. In the case of a river, the surface of the water was slightly disturbed to remove any settled particles before sampling. All the samples were collected and stored in a 500 ml plastic bottle. The coordinates of each sampling point were recorded for proper identification, and the samples were labeled SW1-SW7, EW1-EW7, and PW1-PW6, respectively (refer to Table 1 for the coded samples). The samples were later transported to the Radiation and Health Physics Laboratory, Ladoke Akintola University of Technology, for spectrometry analysis. Figure 2 shows the different water sources where samples were collected in the current area of study, and Figure 3 explains the stages involved in water contamination from the mining activities noticeable in the study areas. Table 1 The sampling site coordinates and elevation and physicochemical parameters of water samples Sample ID Longitude Latitude Elevation (m) Source pH Conductivity (µS/cm) TDS* SW 1 006 o 37'10.5" 09 o 39'31.3" 356 Borehole 6.4 210 90 SW 2 006 o 37'13.2" 09 o 39'40.6" 348 Dug-well 6.8 580 280 SW 3 006 o 39'35.5" 09 o 37'12.0" 354 Borehole 7.0 580 280 SW 4 006 o 37'15.0" 09 o 39'01.2" 347 Borehole 6.8 430 200 SW 5 006 o 37'24.5" 09 o 39'01.5" 403 Borehole 7.2 230 500 SW 6 006 o 36'53.6" 09 o 38'01.0" 341 Stream 7.2 1020 110 SW 7 006 o 36'54.8" 09 o 38'04.8" 340 Borehole 7.4 820 400 PW I 006 o 45'53.6" 09 o 43'34.1" 386 Borehole 7.5 390 190 PW II 006 o 46'00.7" 09 o 43'32.9" 381 Borehole 7.4 320 150 PW III 006 o 45'57.1" 09 o 43'36.4" 380 Dug-well 7.5 260 120 PW IV 006 o 45'58.6" 09 o 43'48.5" 389 Borehole 7.5 410 190 PW V 006 o 46'59.1" 09 o 45'02.3" 340 Dug-well 7.0 320 150 PW VI 006 o 46'48.4" 09 o 45'05.4" 390 River 6.7 100 40 PW VII 006 o 45'53.6" 09 o 43'34.1" 386 Dug-well 6.8 750 360 EW A 006 o 19'06.8" 09 o 22'42.0" 136 Dug-well 7.1 540 250 EW B 006 o 19'09.7" 09 o 22'39.9" 133 Borehole 7.0 420 190 EW C 006 o 18'59.3" 09 o 22'36.3" 141 River 7.4 470 220 EW D 006 o 19'14.1" 09 o 22'47.3" 125 Dug-well 7.2 720 350 EW E 006 o 20'50.1" 09 o 23'11.5" 135 Borehole 7.5 460 220 EW F 006 o 20'59.8" 09 o 22'45.0" 147 Dug-well 7.7 300 130 EW F 006 o 20'58.0" 09 o 23'17.5" 125 Borehole 6.4 210 90 *: TDS: Total Dissolved Solids 2.3 Sample Preparation Before the commencement of sample measurement by spectrometry, an empty Marinelli container was washed with clean water from the dug well and rinsed with HCl acid in order to avoid any contamination [ 7 ], and thereafter the samples were counted for 36000 seconds and recorded for the background counts. The water samples were kept for 30 days to enable the short-lived radionuclides to decay, allowing the radionuclides of interest to attain secular equilibrium. Gamma spectrometric analysis of the water samples to determine the concentrations of radionuclides of interest was then carried out at the Radiation and Health Physics Laboratory, Ladoke Akintola University of Technology, Nigeria. Each water sample was placed close to the detector in succession and counted for 36000 seconds. For each measurement, the photopeaks of 214 Pb (295.3 keV) and 214 Bi (1764.5 keV) for 226 Ra ( 238 U); 212 Pb (238.6 keV), 228 Ac (911.1 keV), and 208 Tl (2614.7 keV) for 232 Th; and 137 Cs (661.7 keV) and that of 40 K (1460.0 keV) were recorded, as well as the activity concentration of each analyzed water sample [ 8 ]. A 3X3 inch sodium iodide (NaI(TI)) detector (by Princeton Gamma Tech., USA) was used to analyze the water samples for the determination of the activity concentration of 40 K, 226 Ra and 232 Th. The multichannel analyzer with model number GS-2000 Pro was shielded in a 100 mm-thick lead connected directly to a computer display. Both gamma-ray spectra analysis and data acquisition were carried out with the help of the Thermion software. Energy calibration was achieved by examining the spectrum of the gamma radiation emitted by a point source of well-defined energies for 36000 seconds. The calibration techniques are explicitly explained in the IAEA report series [ 9 ]. The efficiency of the detector was obtained by means of a standard source with known radionuclide activity [ 10 ]. Equation ( 1 ) [ 11 ] was utilized to calculate the specific activity (As) in Bq l − 1 of each NORM ( 40 K, 226 Ra, and 232 Th) present in the water samples collected from various water sources in the area of study, and the results for the activity concentration are presented in Table 4 . $$\:{A}_{s}=\frac{{N}_{\omega\:s}}{{\gamma\:}_{E}\times\:\epsilon\:\times\:{C}_{t}\times\:m}$$ 1 where \(\:{N}_{\omega\:s}\) is the net counts of the radionuclide in the water samples, \(\:{\gamma\:}_{E}\) is the probability of a gamma ray emission, \(\:\epsilon\:\) is the counting efficiency of the NaI(TI) detector, \(\:m\) is the mass of the water samples and \(\:{C}_{t}\) is the counting time. The extent of radiological hazards posed by the radionuclides present in the water samples from the area of study was estimated by combining the specific activity \(\:{A}_{s}\) and the required conversion factors. 2.4 Estimation of annual effective dose The effect of naturally occurring radionuclides of interest ( 40 K, 226 Ra and 232 Th) in water samples collected from various dug-well were estimated from the water intake by individual. The equation used for this calculation is given by: [ 12 ] $$\:D=CIE$$ 2 where D represents the annual effective dose of an individual intake of NORMs in dug-well water measured in Sv y − 1 , C is the activity concentration of ingested NORMs measured in Bq l − 1 , I is the annual water consumption of an individual measured in l y − 1 , and E is the ingested conversion factor, which varies with respect to the individual age and the type of radioisotopes. Therefore, the cumulative effects of NORMs in the dug-well water depend on the type of radioisotopes in the samples. The conversion factor value, E, was taken from the European Commission Directives (EC-Directives) 97/43/Euratom and 2003/122/Euratom (see Table 2 ) [ 13 ]. To determine the cumulative effects of NORMs in the water samples per annum, the annual effective dose was estimated for each age category: for infants (age < 1) years, children (age from 1 to 12 years), tweens (age from 12 to 17 years), adolescence (age from 12 to 17 years ), and adult (age 17 years and above) with an annual water intake of 200, 300, 350, 600, 700, and 731 l y⁻¹, respectively [ 9 ] 3. Results and discussion The activity concentration of NORMs ( 40 K, 226 Ra, and 232 Th) measured in the three areas of study is shown in Table 2. Man-made radionuclides such as 137 Cs [15] were not detected in all the water samples analyzed from the three areas of study. The activity concentration of 40 K, 226 Ra, and 232 Th varies between the range of 0.35 ± 0.03 to 33.28 ± 2.97, 1.32 ± 0.07 to 3.06 ± 0.18, and 0.85 ± 0.14 to 2.41 ± 0.15 Bq l⁻¹ with a mean of 12.29 Bq l⁻¹, 2.09 Bq l⁻¹, and 1.57 Bq l⁻¹ for all water samples, respectively. The results for the measurements of the pH of all the water samples fall within the range of 6.4 to 7.7 (Table 1), indicating that none of the water samples is pure water; all the water samples are slightly basic solutions [16]. However, the water sample from SW1 is slightly acidic with a value of 6.4. This could be because of the shallow aquifer of the borehole in this area, enabling the radionuclides from the opencast mining activities within the area to leach into the water sources, thereby increasing the pH level [17]. It is observed that in borehole water, the mean activity concentration values for 226 Ra were 2.09. The highest activity concentration for 226 Ra when compared with other locations were found at sample location SW5. This is an indication that the concentration of 226 Ra increases from hilltops to the valleys, which could be attributed to the high elevation of SW5 (403 m above the sea level), as shown in Table 1 [18]. The estimated concentration values of 226 Ra in this study are above the recommended limits by WHO (set at 1.0 Bq l⁻¹) [19] and USEPA (set at 0.185 Bq l⁻¹) [20]. These results further show that the concentration obtained is greater than some results found in other parts of the world, as shown in Table 5. Similarly, 40 K concentration in water sources was found to be 12.29 Bq l⁻¹, which is higher than the set limits by WHO (set at 1.0 Bq l⁻¹) [20] and USEPA (set at 0.185 Bq l⁻¹) [[21] standards, respectively. These results are higher than most of the results reported from other studies, as shown in Table 5. The high concentration level of 40 K could be because of the large deposit of granite rocks and some portion of clay soil formation in the three areas of study, especially in the Shakwata area of study. This is in agreement with the findings of Ajayi and Owolabi (2007) [22]. The calculated annual effective dose shown in Table 6 as well as the total annual effective dose shown in Table 7 were estimated for the ICRP five age groups between 0-1, 2-7, 7-12, 12-17, and 17, indicating an age-dependent relationship [23]. The variation of annual effective dose (mSv y⁻¹) due to the consumption of 40 K in water was 0.43 41.27, 0.22 19.61, 0.16 15.14, 1.60 151.76, and 1.58 150.63 mSv y⁻¹ for infants (age <1 year), children (age 2-7 years), tweens (age 7-12 years), adolescents (age 12-17 years), and adults (age 18 years), respectively. On the other hand, the variation of annual effective dose (mSv y⁻¹) due to the consumption of 226 Ra in water with age dependence was given by 1.64 3.79, 0.86 1.93 0.60, 1.39 6.20, 13.95, and 5.97 3.85 mSv y⁻¹ for infant (age <1 year), children (age 2-7 years), tween (age 7-12 years), adolescence (age 12-17 years), and adult (age 17 years and above), respectively. Furthermore, the variation of annual effective dose (mSv y -1 ) due to the intake of 232 Th in water was outlined as 1.05 2.99, 0.54 .52, 0.39 1.10, 3.88 10.99, and 3.85 10.91 in mSv y -1 for infant (age 17 years), respectively. It was evident from the results (Table 4) that 40 K has the highest mean radionuclide concentration of about 12.29 Bq l⁻¹, followed by 226 Ra and 232 Th, respectively. This was in agreement with the results obtained by Ayodele et. al. (2017) [24]. The results for the annual effective dose variation of 40 K, 226 Ra, and 232 Th consumption in water per age group show that (in all ages) the concentration obtained in this study exceeds the standard reference limits of 10, 1.0, and 0.1 mSv y⁻¹ set by the WHO guidelines [14], [20], and ICRP [25], respectively. However, the annual effective dose of 232 Th found in SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2 7 and 7 12 was below the recommended limit of 1.0 mSv y⁻¹ set by ICRP (see Table 6) [25]. The results presented in Table 6 show that the highest contribution to the annual effective dose comes from potassium and radium ( 40 K and 226 Ra). This indicates that the annual water consumption level and ingestion of 40 K and 226 Ra between ages 12-17 years and 17 years and above is higher than the annual water consumption and ingestion of 40 K and 226 Ra between ages 0-1 years, 2-7 years and 7-12 years. By extension, this increases the annual effective dose in ages 12-17 years and 17 years and above when compared to the annual effective dose of the other age groups. The high concentration of 40 K and 226 Ra in the water samples suggests that proper measures need to be in place in these communities. To ensure safe drinking water, communities should implement purification method such as reverse osmosis, as recommended by the USEPA [26] and WHO [27]. This is because these radionuclides are radiotoxic, especially 226 Ra, and if ingested by children and babies, it can affect their bones and lead to bone cancers [28]. Figure 4 show a bar chart comparison between total annual effective dose and the five age groups. In addition, Figure 5 further indicates that the annual effective dose is higher in the age groups between 12-17 years and 17 years and above. This could be because of the high ingestion of 226 Ra and 40 K in water by age groups 12-17 years and 17 years above as compared to age groups 0-1 years, 2-7 years and 7-12 years, respectively. Table 5. Comparison of the activity concentration of the three prevalent naturally occurring radionuclides ( 40 K, 226 Ra, and 232 Th) in water sources in the present study with results obtained from some countries in other parts of the world. Origin Activity concentration (Bq l -1 ) Reference 40 K 226 Ra 232 Th Nigeria (Niger State) preset study 0.35–33.28 1.32–3.06 0.85–2.41 Present work Turkey (EasternBlackSea) 0.009–0.290 0.003–0.045 NDA [29] Nigeria (Ondo State) 35.814–70.380 6.428–12.586 1.589–3.752 [24] Iran (North Guilan) NDA 0.0037 0.0081 NDA [30] Ghana 0.52 0.870 0.460 0.76 1.330 [31] Iran NDA 0.002 NDA [32] Saudi Arabia 5.640 1.520 0.810 [33] Finland NDA 0.015–0.018 0.006–0.009 [34] Jordan 0.194 0.131 0.049 [18] WHO 10 1 0.1 [14] Table 6. Annual effective dose (mSv y -1 ) for different age groups Table 7. Total annual effective dose (mSv y -1 ) from 40 K, 226 Ra and 232 Th water samples Sample code Age group (years) 0 1 2 7 7 12 12 17 > 17 SW 1 24.24 12.32 8.90 89.15 88.48 SW 2 8.94 4.54 3.28 32.88 32.63 SW 3 5.72 2.90 2.10 21.02 20.86 SW 4 10.96 5.57 4.02 40.31 40.01 SW 5 39.72 20.18 14.57 146.06 144.97 SW 6 28.26 14.36 10.37 103.92 103.15 SW 7 17.50 8.89 6.42 64.34 63.86 PW I 27.33 13.89 10.03 100.50 99.75 PW II 8.41 4.27 3.08 30.92 30.69 PW III 41.33 21.00 15.17 151.98 150.85 PW IV 8.07 4.10 2.96 29.69 29.46 PW V 45.59 23.17 16.73 167.67 166.42 PW VI 17.51 8.90 6.42 64.39 63.91 PW VII 42.16 21.42 15.47 155.04 153.88 EW A 7.66 3.89 2.81 28.18 27.97 EW B 20.51 10.42 7.53 75.42 74.86 EW C 12.61 6.41 4.63 46.38 46.03 EW D 7.42 3.77 2.72 27.27 27.07 EW E 5.69 2.89 2.09 20.93 20.77 EW F 15.91 8.08 5.84 58.50 58.07 Max. 56.99 28.95 20.91 209.58 208.01 Min. 34.11 17.33 12.52 125.45 124.51 Mean 44.55 22.63 16.35 163.83 162.61 4. Conclusion In this study, the naturally occurring radionuclides (NORMs) such as 40 K, 226 Ra, and 232 Th were examined in four different drinking water sources (borehole, dug well, stream, and river) in three communities in Niger State, north-central Nigeria. No man-made radionuclide ( 137 Cs) was detected in all the water samples. The results for the measured activity concentration ranged from 0.35 ± 0.03 to 33.28 ± 2.97, 1.32 ± 0.07 to 3.06 ± 0.18, and 0.85 ± 0.14 to 2.41 ± 0.15 Bq l⁻¹ with a mean of 12.29 Bq l⁻¹, 2.09 Bq l⁻¹, and 1.57 Bq l⁻¹ for 40 K, 226 Ra, and 232 Th, respectively. The results for the activity concentration were all above the recommended limits set by ICRP and WHO. It was found that 40 K has the highest contribution to the concentration of radionuclides in drinking water sources because of the large deposit of granite rocks mined in the communities. The results for the five groups age-dependent relationship with total annual effective dose with respect to the intake of radionuclides in water sources in the three communities were found to be above the recommended set standard limits of 1.0 mSv y⁻¹ and 0.1 mSv y⁻¹ by ICRP and WHO, except in sample locations SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2-7 years and 7-12 years, where the 232 Th level was found to be below the recommended limit of 1.0 mSv y⁻¹ set by ICRP. Therefore, considering that the annual effective dose exceeds the WHO recommended guideline of 0.1 mSv y⁻¹, the water sources from the mining areas should be restricted to domestic use and not for public consumption except after treatment where necessary. Declarations Author contributions section Abdullahi Muhammad: experiments, data processing, original draft, manuscript preparation, Akbar Abbasi: Project supervision, software analysis, manuscript revision, manuscript writing. Funding Financial support was not provided for this research. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to extend their deepest gratitude to Prof. Izzet SAKALI, the supervisor thesis research, for his invaluable guidance, support, and encouragement throughout the study. Sincere thanks are also given to the Radiation Protection Section of Nigeria for providing laboratory facilities, as well as to Eastern Mediterranean University and the University of Kyrenia for their academic and financial support during the completion of this Ph.D. thesis. References Sources and effects of ionizing radiation : United Nations Scientific Committee on the Effects of Atomic Radiation : UNSCEAR 2000 report to the General Assembly, with scientific annexes. . Vol. 1, Sources . United Nations, 2000. F. O. 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Babatunde, and O. E. Orisakwe, “Natural occurring radioactive materials (NORMs) from mining sites in Nigeria: A systematic review of geographical distribution and public health concern,” Aug. 01, 2022, Elsevier Ltd . doi: 10.1016/j.jenvrad.2022.106889. U. Çevik, N. Damla, G. Karahan, N. Çelebi, and A. I. Kobya, “Natural radioactivity in tap waters of Eastern Black Sea region of Turkey,” Radiat Prot Dosimetry , vol. 118, no. 1, pp. 88–92, Apr. 2006, doi: 10.1093/rpd/nci325. A. Abbasi and F. Mirekhtiary, “Gross alpha and beta exposure assessment due to intake of drinking water in Guilan, Iran,” J Radioanal Nucl Chem , vol. 314, no. 2, pp. 1075–1081, Nov. 2017, doi: 10.1007/s10967-017-5493-6. A. Faanu et al. , “Radiological landscape of natural resources and mining: Unveiling the environmental impact of naturally occurring radioactive materials in Ghana’s mining areas,” Heliyon , vol. 10, no. 3, Feb. 2024, doi: 10.1016/j.heliyon.2024.e24959. A. Abbasi and S. F. Mirekhtiary, “Risk assessment due to various terrestrial radionuclides concentrations scenarios,” Int J Radiat Biol , vol. 95, no. 2, pp. 179–185, Feb. 2019, doi: 10.1080/09553002.2019.1539881. A. H. Al-Ghamdi, “Radioactivity Measurements and Radiation Dose Assessments in Ground Water of Al-Baha Region, Saudi Arabia,” Journal of Geoscience and Environment Protection , vol. 07, no. 10, pp. 112–119, 2019, doi: 10.4236/gep.2019.710009. P. Vesterbacka, I. Mäkeläinen, and H. Arvela, “Natural radioactivity in drinking water in private wells in Finland,” Radiat Prot Dosimetry , vol. 113, no. 2, pp. 223–232, 2005, doi: 10.1093/rpd/nch446. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6889551","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471005464,"identity":"962e25ed-f20c-496f-8ca9-d0335f0b2b52","order_by":0,"name":"Abdullahi Muhammad","email":"","orcid":"https://orcid.org/0000-0003-4292-7536","institution":"Eastern Mediterranean University","correspondingAuthor":false,"prefix":"","firstName":"Abdullahi","middleName":"","lastName":"Muhammad","suffix":""},{"id":471005465,"identity":"ab7896ac-0ff9-42f5-b3a4-995168a0350e","order_by":1,"name":"Akbar","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4664-3744","institution":"Kyrenia University","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Akbar","suffix":""}],"badges":[],"createdAt":"2025-06-13 15:44:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6889551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6889551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84702355,"identity":"2a0f594c-0423-4851-8e96-846298f275b3","added_by":"auto","created_at":"2025-06-16 11:40:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194145,"visible":true,"origin":"","legend":"\u003cp\u003eThe geographical position of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/c01d01ce086256ce09c1aa48.png"},{"id":84703608,"identity":"266f2047-55db-4231-af54-4c74148b3807","added_by":"auto","created_at":"2025-06-16 11:56:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336312,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A dug-well at Pina community water sampling, Shiroro local government area, (b) River flowing across Eregi community in Katcha local government area with a source from the famous River Niger, (c) Ongoing water sampling from a borehole at Pina in Shiroro local government area, (d) A stream flowing across an opencast mining site at Shakwata, Bosso local government area.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/2fbb7460abb9c350ebbc3959.png"},{"id":84702515,"identity":"d7a2e9c0-62af-48ea-84f2-f9411d2570f2","added_by":"auto","created_at":"2025-06-16 11:48:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80996,"visible":true,"origin":"","legend":"\u003cp\u003eStages of NORMs contamination from a mining site to water sources used in the human habitat.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/3c59efd13a73ddb91c110579.png"},{"id":84702356,"identity":"6c53238d-d6d7-4910-9089-0373e2e2634e","added_by":"auto","created_at":"2025-06-16 11:40:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58992,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison between total annual effective doses (mSv y\u003csup\u003e-1\u003c/sup\u003e) from the radionuclides\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/df71540bf08735bb957f4e4d.png"},{"id":84702517,"identity":"52b73956-2e50-4b30-8246-f1b347777f56","added_by":"auto","created_at":"2025-06-16 11:48:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11677,"visible":true,"origin":"","legend":"\u003cp\u003eThe graph of total annual effective doses by age group\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/bf4b735f61468b26052859ec.png"},{"id":84704040,"identity":"df16b156-32dc-43ea-8bab-640e287effed","added_by":"auto","created_at":"2025-06-16 12:04:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1725802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6889551/v1/dcff4b1e-5f34-4185-b269-82cb7f91777d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNatural Radioelements Concentration Analysis and Risk Assessment in Drinking Water of Mining Area Niger State, North Central Nigeria\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs the saying goes, water is life. Every living organism on earth requires water for daily life activity and survival, which implies that water is an essential component of the human life cycle. No wonder, in the past two decades, decision makers from different countries across the globe have carefully treated the study of naturally occurring radionuclide materials (NORMs) in the environment and how it interacts with the human population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Being an important component of life on earth, water must be treated properly, preserved, and distributed accordingly [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The study of natural radioactivity concentration in drinking water and its sources can be traced back to the 1950s and beyond, as evident from the work of Hursh [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis is growing public concern as the sources of naturally occurring radionuclides (not only in water) increase as well as their effects. Some notable sources of naturally occurring radionuclides include \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, \u003csup\u003e232\u003c/sup\u003eTh, and their decay daughter nuclides. These radionuclides are found in different concentrations in most ground formations, such as granite rocks. According to Shabana and Kinsara (2014) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], all granitic compositions usually have a higher level of natural radioactivity than rocks with other compositions. On this note, all surface and groundwater are expected to be composed of a higher concentration of one or more of the natural radionuclides depending on the depth to the basement of the aquifer, the nature of the aquifer rocks, the characteristics of the water involved, and the local geology of the area (soil, rock, and clay). These radionuclides can contaminate surface and underground water through rock-water interaction and migration of radionuclides, which in turn exposes the users to radiation doses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, other sources of radioactivity in water sources span from the mining of mineral resources by humans, nuclear and non-nuclear accidents, testing of nuclear atomic bombs, and use of radioisotopes in nuclear medicine. The areas of study are characterised with verse arable land for farming and a prevailing scattered opencast mining activity, which is a major occupation of the people and this raises many concerns as the safety of the water bodies in the habitat is at risk due to the release of natural radionuclides by the activities of the miners and this reaches the larger population through the food chain.\u003c/p\u003e \u003cp\u003eHealth risk can arise from the ingestion or inhalation of a small amount of decayed radionuclides in drinking water, which exposes the human internal body to ionizing radiation. Since the number of NORMs present in drinking water is directly linked to the doses of ionizing radiation received by an individual, it is paramount to accurately measure the activity concentration of each NORMs ingested by the human population as well as via domestic uses.\u003c/p\u003e \u003cp\u003eThis study presents the spectrometry analysis of different gamma-emitting radionuclides using a high-resolution coaxial sodium iodide detector (NaI(TI)). The radionuclide concentrations in \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh in 20 representative water samples collected from scattered dug wells from three communities (Shakwata, Pina, and Eregi) in Niger State, north-central Nigeria, were further checked with the international set limits for natural radionuclides in water sources used for drinking. The findings from this study may serve as a database for future reference.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n \u003cp\u003eThe research study areas are located in the Shakwata community (Bosso local government area of Niger State), Eregi (Katcha local government area of Niger State), and Pina (Shiroro local government area of Niger State) in North Central Nigeria. Shakwata is a border community to Minna, the capital of Niger State. It is situated along the Minna-Shiroro expressway at latitude 009\u0026deg;39\u0026apos;31.3\u0026quot;N and longitude 006\u0026deg;37\u0026apos;10.5\u0026quot;E. It is 11 km from Minna.\u003c/p\u003e\n \u003cp\u003eIn the same vein, Eregi is a border community between Bida and Minna. It is located along 009\u0026deg;22\u0026apos;42.0\u0026quot;N latitude and 006\u0026deg;19\u0026apos;06.8\u0026quot;E longitude. Farming and fishing is the main occupation of the people of this community. It is about 127 km from Minna along the Minna-Bida expressway, and it is bounded in the south by the great River Niger.\u003c/p\u003e\n \u003cp\u003eOn the other hand, Pina is a border community between Shiroro and Minna. It is located at latitude 009\u0026deg;43\u0026apos;34.1\u0026quot;N and longitude 006\u0026deg;45\u0026apos;53.6\u0026quot;E. Farming, mineral exploration, and fishing are the major occupations of the people of this community. It is about 74 km from Minna along the Minna-Shiroro expressway. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location map and the sample collection points [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the study areas are characterized by hard rocks of Precambrian basement complex rocks, which spans towards the northern edge of the Niger trough. These basement complex rocks are older granites, the migmatites gneiss rocks, and schist belts. At the southern part, the Precambrian rocks forms a layer with the sedimentary rocks (cretaceous in age) and the alluvium soil formation found at the River Niger border and its tributaries [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sample Collection\u003c/h2\u003e\n \u003cp\u003eTwenty (20) scattered representative water samples were collected from functional dug wells and rivers in the three areas of study (Shakwata, Pina, and Eregi) at different locations, as seen in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The sampling locations were selected based on the population density and the usability of the wells. Water samples were collected in the rainy season between May and August 2024, and the most active and frequently used dug wells and boreholes were considered. Before the water sampling, each dug well was allowed to run for 3 minutes before sample collection. In the case of a river, the surface of the water was slightly disturbed to remove any settled particles before sampling. All the samples were collected and stored in a 500 ml plastic bottle. The coordinates of each sampling point were recorded for proper identification, and the samples were labeled SW1-SW7, EW1-EW7, and PW1-PW6, respectively (refer to Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for the coded samples). The samples were later transported to the Radiation and Health Physics Laboratory, Ladoke Akintola University of Technology, for spectrometry analysis.\u003c/p\u003e\n \u003cp\u003eFigure 2 shows the different water sources where samples were collected in the current area of study, and Figure 3 explains the stages involved in water contamination from the mining activities noticeable in the study areas.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe sampling site coordinates and elevation and physicochemical parameters of water samples\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity (\u0026micro;S/cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e37\u0026apos;10.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e39\u0026apos;31.3\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e37\u0026apos;13.2\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e39\u0026apos;40.6\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e39\u0026apos;35.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e37\u0026apos;12.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e37\u0026apos;15.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e39\u0026apos;01.2\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e37\u0026apos;24.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e39\u0026apos;01.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e36\u0026apos;53.6\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e38\u0026apos;01.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e36\u0026apos;54.8\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e38\u0026apos;04.8\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 45\u0026apos;53.6\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e43\u0026apos;34.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 46\u0026apos;00.7\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e43\u0026apos;32.9\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e45\u0026apos;57.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e43\u0026apos;36.4\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e45\u0026apos;58.6\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e43\u0026apos;48.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 46\u0026apos;59.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e45\u0026apos;02.3\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW VI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 46\u0026apos;48.4\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e45\u0026apos;05.4\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW VII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 45\u0026apos;53.6\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e43\u0026apos;34.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e19\u0026apos;06.8\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e22\u0026apos;42.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e 19\u0026apos;09.7\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e22\u0026apos;39.9\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e18\u0026apos;59.3\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e22\u0026apos;36.3\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e19\u0026apos;14.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e22\u0026apos;47.3\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e20\u0026apos;50.1\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e23\u0026apos;11.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e20\u0026apos;59.8\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e22\u0026apos;45.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDug-well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEW F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e006\u003csup\u003eo\u003c/sup\u003e20\u0026apos;58.0\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09\u003csup\u003eo\u003c/sup\u003e 23\u0026apos;17.5\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBorehole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*: TDS: Total Dissolved Solids\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Sample Preparation\u003c/h2\u003e\n \u003cp\u003eBefore the commencement of sample measurement by spectrometry, an empty Marinelli container was washed with clean water from the dug well and rinsed with HCl acid in order to avoid any contamination [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], and thereafter the samples were counted for 36000 seconds and recorded for the background counts. The water samples were kept for 30 days to enable the short-lived radionuclides to decay, allowing the radionuclides of interest to attain secular equilibrium.\u003c/p\u003e\n \u003cp\u003eGamma spectrometric analysis of the water samples to determine the concentrations of radionuclides of interest was then carried out at the Radiation and Health Physics Laboratory, Ladoke Akintola University of Technology, Nigeria. Each water sample was placed close to the detector in succession and counted for 36000 seconds. For each measurement, the photopeaks of \u003csup\u003e214\u003c/sup\u003ePb (295.3 keV) and \u003csup\u003e214\u003c/sup\u003eBi (1764.5 keV) for \u003csup\u003e226\u003c/sup\u003eRa (\u003csup\u003e238\u003c/sup\u003eU); \u003csup\u003e212\u003c/sup\u003ePb (238.6 keV), \u003csup\u003e228\u003c/sup\u003eAc (911.1 keV), and \u003csup\u003e208\u003c/sup\u003eTl (2614.7 keV) for \u003csup\u003e232\u003c/sup\u003eTh; and \u003csup\u003e137\u003c/sup\u003eCs (661.7 keV) and that of \u003csup\u003e40\u003c/sup\u003eK (1460.0 keV) were recorded, as well as the activity concentration of each analyzed water sample [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eA 3X3 inch sodium iodide (NaI(TI)) detector (by Princeton Gamma Tech., USA) was used to analyze the water samples for the determination of the activity concentration of \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa and \u003csup\u003e232\u003c/sup\u003eTh. The multichannel analyzer with model number GS-2000 Pro was shielded in a 100 mm-thick lead connected directly to a computer display. Both gamma-ray spectra analysis and data acquisition were carried out with the help of the Thermion software. Energy calibration was achieved by examining the spectrum of the gamma radiation emitted by a point source of well-defined energies for 36000 seconds. The calibration techniques are explicitly explained in the IAEA report series [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. The efficiency of the detector was obtained by means of a standard source with known radionuclide activity [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eEquation (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e] was utilized to calculate the specific activity (As) in Bq l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of each NORM (\u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh) present in the water samples collected from various water sources in the area of study, and the results for the activity concentration are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{A}_{s}=\\frac{{N}_{\\omega\\:s}}{{\\gamma\\:}_{E}\\times\\:\\epsilon\\:\\times\\:{C}_{t}\\times\\:m}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{\\omega\\:s}\\)\u003c/span\u003e\u003c/span\u003e is the net counts of the radionuclide in the water samples, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{E}\\)\u003c/span\u003e\u003c/span\u003e is the probability of a gamma ray emission, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e is the counting efficiency of the NaI(TI) detector, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\)\u003c/span\u003e\u003c/span\u003e is the mass of the water samples and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the counting time.\u003c/p\u003e\u003cp\u003eThe extent of radiological hazards posed by the radionuclides present in the water samples from the area of study was estimated by combining the specific activity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{s}\\)\u003c/span\u003e\u003c/span\u003e and the required conversion factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Estimation of annual effective dose\u003c/h2\u003e\u003cp\u003eThe effect of naturally occurring radionuclides of interest (\u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa and \u003csup\u003e232\u003c/sup\u003eTh) in water samples collected from various dug-well were estimated from the water intake by individual. The equation used for this calculation is given by: [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:D=CIE$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere D represents the annual effective dose of an individual intake of NORMs in dug-well water measured in Sv y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, C is the activity concentration of ingested NORMs measured in Bq l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, I is the annual water consumption of an individual measured in l y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and E is the ingested conversion factor, which varies with respect to the individual age and the type of radioisotopes. Therefore, the cumulative effects of NORMs in the dug-well water depend on the type of radioisotopes in the samples. The conversion factor value, E, was taken from the European Commission Directives (EC-Directives) 97/43/Euratom and 2003/122/Euratom (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. To determine the cumulative effects of NORMs in the water samples per annum, the annual effective dose was estimated for each age category: for infants (age\u0026thinsp;\u0026lt;\u0026thinsp;1) years, children (age from 1 to 12 years), tweens (age from 12 to 17 years), adolescence (age from 12 to 17 years ), and adult (age 17 years and above) with an annual water intake of 200, 300, 350, 600, 700, and 731 l y⁻\u0026sup1;, respectively [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThe activity concentration of NORMs (\u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh) measured in the three areas of study is shown in Table 2. Man-made radionuclides such as \u003csup\u003e137\u003c/sup\u003eCs [15] were not detected in all the water samples analyzed from the three areas of study. The activity concentration of \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh varies between the range of 0.35 \u0026plusmn;\u0026nbsp;0.03 to 33.28 \u0026plusmn;\u0026nbsp;2.97, 1.32 \u0026plusmn;\u0026nbsp;0.07 to 3.06 \u0026plusmn;\u0026nbsp;0.18, and 0.85 \u0026plusmn;\u0026nbsp;0.14 to 2.41 \u0026plusmn;\u0026nbsp;0.15 Bq l⁻\u0026sup1; with a mean of 12.29 Bq l⁻\u0026sup1;, 2.09 Bq l⁻\u0026sup1;, and 1.57 Bq l⁻\u0026sup1; for all water samples, respectively.\u003c/p\u003e\n\u003cp\u003eThe results for the measurements of the pH of all the water samples fall within the range of 6.4 to\u0026nbsp;7.7 (Table 1), indicating that none of the water samples is pure water; all the water samples are slightly basic solutions [16]. However, the water sample from SW1 is slightly acidic with a value of 6.4. This could be because of the shallow aquifer of the borehole in this area, enabling the radionuclides from the opencast mining activities within the area to leach into the water sources, thereby increasing the pH level [17]. It is observed that in borehole water, the mean activity concentration values for \u003csup\u003e226\u003c/sup\u003eRa were 2.09. The highest activity concentration for \u003csup\u003e226\u003c/sup\u003eRa when compared with other locations were found at sample location SW5. This is an indication that the concentration of \u003csup\u003e226\u003c/sup\u003eRa increases from hilltops to the valleys, which could be attributed to the high elevation of SW5 (403 m above the sea level), as shown in Table 1 [18]. The estimated concentration values of \u003csup\u003e226\u003c/sup\u003eRa in this study are above the recommended limits by WHO (set at 1.0 Bq l⁻\u0026sup1;) [19] \u0026nbsp;and USEPA (set at 0.185 Bq l⁻\u0026sup1;) [20].\u0026nbsp;These results further show that the concentration obtained is greater than some results found in other parts of the world, as shown in Table 5. Similarly, \u003csup\u003e40\u003c/sup\u003eK concentration in water sources was found to be 12.29 Bq l⁻\u0026sup1;, which is higher than the set limits by WHO (set at 1.0 Bq l⁻\u0026sup1;) [20]\u0026nbsp;\u0026nbsp;and\u0026nbsp;USEPA (set at 0.185 Bq l⁻\u0026sup1;) [[21]\u0026nbsp;standards, respectively.\u0026nbsp;These results are higher than most of the results reported from other studies, as shown in Table 5. The high concentration level of \u003csup\u003e40\u003c/sup\u003eK could be because of the large deposit of granite rocks and some portion of clay soil formation in the three areas of study, especially in the Shakwata area of study. This is in agreement with the findings of Ajayi and Owolabi (2007)\u0026nbsp;[22].\u003c/p\u003e\n\u003cp\u003eThe calculated annual effective dose shown in Table 6 as well as the total annual effective dose shown in Table 7 were estimated for the ICRP five age groups between 0-1, 2-7, 7-12, 12-17, and\u0026nbsp;17, indicating an age-dependent relationship [23]. The variation of annual effective dose (mSv y⁻\u0026sup1;) due to the consumption of \u003csup\u003e40\u003c/sup\u003eK in water was 0.43\u0026nbsp;41.27, 0.22\u0026nbsp;19.61, 0.16\u0026nbsp;15.14, 1.60\u0026nbsp;151.76, and 1.58\u0026nbsp;150.63 mSv y⁻\u0026sup1; for infants (age \u0026lt;1 year), children (age 2-7 years), tweens (age 7-12 years), adolescents (age 12-17\u0026nbsp;years), and adults (age 18 years), respectively. On the other hand, the variation of annual effective dose (mSv y⁻\u0026sup1;) due to the consumption of \u003csup\u003e226\u003c/sup\u003eRa in water with age dependence was given by 1.64\u0026nbsp;3.79, 0.86\u0026nbsp;1.93\u0026nbsp;0.60,\u0026nbsp;1.39\u0026nbsp;6.20,\u0026nbsp;13.95, and 5.97\u0026nbsp;3.85 mSv y⁻\u0026sup1; for infant (age \u0026lt;1 year), children (age 2-7 years), tween (age 7-12 years), adolescence (age 12-17\u0026nbsp;years), and adult (age 17 years and above), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the variation of annual effective dose (mSv y\u003csup\u003e-1\u003c/sup\u003e) due to the intake of \u003csup\u003e232\u003c/sup\u003eTh in water was outlined as 1.05\u0026nbsp;2.99, 0.54\u0026nbsp;\u0026nbsp;.52, 0.39\u0026nbsp;1.10, 3.88\u0026nbsp;10.99, and 3.85\u0026nbsp;10.91 in mSv y\u003csup\u003e-1\u003c/sup\u003e for infant (age \u0026lt;1 year) children (age\u0026nbsp;2-7 years), tween (age 7-12 years), adolescence (age 12-17 years), and adult (age \u0026gt;17 years), respectively. It was evident from the results (Table 4) that \u003csup\u003e40\u003c/sup\u003eK has the highest mean radionuclide concentration of about\u0026nbsp;12.29 Bq l⁻\u0026sup1;, followed by \u003csup\u003e226\u003c/sup\u003eRa and \u003csup\u003e232\u003c/sup\u003eTh, respectively. This was in agreement with the results obtained by Ayodele et. al. (2017) [24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results for the annual effective dose variation of \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh consumption in water per age group show that (in all ages) the concentration obtained in this study exceeds the standard reference limits of 10, 1.0, and 0.1 mSv y⁻\u0026sup1; set by the WHO guidelines [14], [20], and ICRP [25], respectively. However, the annual effective dose of \u003csup\u003e232\u003c/sup\u003eTh found in SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2\u0026nbsp;7 and 7\u0026nbsp;12 was below the recommended limit of 1.0 mSv y⁻\u0026sup1; set by ICRP (see Table 6)\u0026nbsp;[25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results presented in Table 6 show that the highest contribution to the annual effective dose comes from potassium and radium (\u003csup\u003e40\u003c/sup\u003eK and \u003csup\u003e226\u003c/sup\u003eRa). This indicates that the annual water consumption level and ingestion of \u003csup\u003e40\u003c/sup\u003eK and \u003csup\u003e226\u003c/sup\u003eRa between ages 12-17 years and 17 years and above is higher than the annual water consumption and ingestion of \u003csup\u003e40\u003c/sup\u003eK and \u003csup\u003e226\u003c/sup\u003eRa between ages 0-1 years, 2-7 years and 7-12 years. By extension, this increases the annual effective dose in ages 12-17 years and 17 years and above when compared to the annual effective dose of the other age groups. The high concentration of \u003csup\u003e40\u003c/sup\u003eK and \u003csup\u003e226\u003c/sup\u003eRa in the water samples suggests that proper measures need to be in place in these communities. To ensure safe drinking water, communities should implement purification method such as reverse osmosis, as recommended by the USEPA [26] and WHO [27]. This is because these radionuclides are radiotoxic, especially \u003csup\u003e226\u003c/sup\u003eRa, and if ingested by children and babies, it can affect their bones and lead to bone cancers [28].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 show a bar chart comparison between total annual effective dose and the five age groups. In addition, Figure 5 further indicates that the annual effective dose is higher in the age groups between 12-17 years and 17 years and above. This could be because of the high ingestion of \u003csup\u003e226\u003c/sup\u003eRa and \u003csup\u003e40\u003c/sup\u003eK in water by age groups 12-17 years and 17 years above as compared to age groups 0-1 years, 2-7 years and 7-12 years, respectively.\u003c/p\u003e\n\u003cp\u003eTable 5. Comparison of the activity concentration of the three prevalent naturally occurring radionuclides (\u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh) in water sources in the present study with results obtained from some countries in other parts of the world.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity concentration (Bq l\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e40\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e226\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eRa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e232\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eTh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNigeria (Niger State) preset study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.35\u0026ndash;33.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.32\u0026ndash;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.85\u0026ndash;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003ePresent work\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eTurkey (EasternBlackSea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.009\u0026ndash;0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.003\u0026ndash;0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNigeria (Ondo State)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e35.814\u0026ndash;70.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e6.428\u0026ndash;12.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.589\u0026ndash;3.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eIran (North Guilan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.0037\u0026nbsp;0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.52\u0026nbsp;0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.76\u0026nbsp;1.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;5.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eNDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.015\u0026ndash;0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.006\u0026ndash;0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 6. Annual effective dose (mSv\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003ey\u003csup\u003e-1\u003c/sup\u003e) for different age groups\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 7. Total annual effective dose (mSv y\u003csup\u003e-1\u003c/sup\u003e) from \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa and \u003csup\u003e232\u003c/sup\u003eTh water samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ecode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt; 17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;24.24\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;12.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;89.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;88.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;32.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;32.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;21.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;20.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;39.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;20.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;14.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;146.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;144.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;28.26\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;14.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;103.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;103.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSW 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;17.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;64.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;63.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;27.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;100.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;99.75\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;30.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;30.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;41.33\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;21.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;15.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;151.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;150.85\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.96\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;29.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;29.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;45.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;23.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;16.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;167.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;166.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW VI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;17.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;64.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;63.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003ePW VII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;42.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;21.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;15.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;155.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;153.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 7.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;28.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;27.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;20.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 7.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;75.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;74.86\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;12.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 6.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;46.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;46.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 7.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;27.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;27.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;20.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;20.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eEW F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;15.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;58.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;58.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e56.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e28.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e209.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e208.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e34.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e17.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e12.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e125.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e124.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 99px;\"\u003e\n \u003cp\u003e44.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e22.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 101px;\"\u003e\n \u003cp\u003e16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e163.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e162.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, the naturally occurring radionuclides (NORMs) such as \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh were examined in four different drinking water sources (borehole, dug well, stream, and river) in three communities in Niger State, north-central Nigeria. No man-made radionuclide (\u003csup\u003e137\u003c/sup\u003eCs) was detected in all the water samples. The results for the measured activity concentration ranged from 0.35 ±\u0026nbsp;0.03 to 33.28 ±\u0026nbsp;2.97, 1.32 ±\u0026nbsp;0.07 to 3.06 ±\u0026nbsp;0.18, and 0.85 ±\u0026nbsp;0.14 to 2.41 ±\u0026nbsp;0.15 Bq l⁻¹ with a mean of 12.29 Bq l⁻¹, 2.09 Bq l⁻¹, and 1.57 Bq l⁻¹ for \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh, respectively. The results for the activity concentration were all above the recommended limits set by ICRP and WHO. It was found that\u0026nbsp;\u003csup\u003e40\u003c/sup\u003eK has the highest contribution to the concentration of radionuclides in drinking water sources because of the large deposit of granite rocks mined in the communities. The results for the five groups age-dependent relationship with total annual effective dose with respect to the intake of radionuclides in water sources in the three communities were found to be above the recommended set standard limits of 1.0 mSv y⁻¹ and 0.1 mSv y⁻¹ by ICRP and WHO, except in sample locations SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2-7 years and 7-12 years, where the \u003csup\u003e232\u003c/sup\u003eTh level was found to be below the recommended limit of 1.0 mSv y⁻¹ set by ICRP. Therefore, considering that the annual effective dose exceeds the WHO recommended guideline of 0.1 mSv y⁻¹, the water sources from the mining areas should be restricted to domestic use and not for public consumption except after treatment where necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions section\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbdullahi Muhammad:\u003c/strong\u003e experiments, data processing, original draft, manuscript preparation, \u003cstrong\u003eAkbar Abbasi:\u003c/strong\u003e Project supervision, software analysis, manuscript revision, manuscript writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support was not provided for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to extend their deepest gratitude to Prof. Izzet SAKALI, the supervisor thesis research, for his invaluable guidance, support, and encouragement throughout the study. Sincere thanks are also given to the Radiation Protection Section of Nigeria for providing laboratory facilities, as well as to Eastern Mediterranean University and the University of Kyrenia for their academic and financial support during the completion of this Ph.D. thesis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003eSources and effects of ionizing radiation : United Nations Scientific Committee on the Effects of Atomic Radiation : UNSCEAR 2000 report to the General Assembly, with scientific annexes. . Vol. 1, Sources\u003c/em\u003e. United Nations, 2000.\u003c/li\u003e\n \u003cli\u003eF. O. Ugbede, B. C. Aduo, O. N. Ogbonna, and O. C. Ekoh, \u0026ldquo;Natural radionuclides, heavy metals and health risk assessment in surface water of Nkalagu river dam with statistical analysis,\u0026rdquo; \u003cem\u003eSci Afr\u003c/em\u003e, vol. 8, Jul. 2020, doi: 10.1016/j.sciaf.2020.e00439.\u003c/li\u003e\n \u003cli\u003eJ. B. 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Al-Ghamdi, \u0026ldquo;Radioactivity Measurements and Radiation Dose Assessments in Ground Water of Al-Baha Region, Saudi Arabia,\u0026rdquo; \u003cem\u003eJournal of Geoscience and Environment Protection\u003c/em\u003e, vol. 07, no. 10, pp. 112\u0026ndash;119, 2019, doi: 10.4236/gep.2019.710009.\u003c/li\u003e\n \u003cli\u003eP. Vesterbacka, I. M\u0026auml;kel\u0026auml;inen, and H. Arvela, \u0026ldquo;Natural radioactivity in drinking water in private wells in Finland,\u0026rdquo; \u003cem\u003eRadiat Prot Dosimetry\u003c/em\u003e, vol. 113, no. 2, pp. 223\u0026ndash;232, 2005, doi: 10.1093/rpd/nch446.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Eastern Mediterranean University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gamma-Ray Spectrometry, Radiotoxicity, Activity Concentration, Annual Effective Dose, Niger State","lastPublishedDoi":"10.21203/rs.3.rs-6889551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6889551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, gamma spectrometry analysis was performed to determine the activity concentration of \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh in four different water sources in three different communities (Shakwata, Eregi, and Pina) in Niger State, north-central Nigeria. Total 20 water samples were analyzed. The activity concentration of \u003csup\u003e40\u003c/sup\u003eK, \u003csup\u003e226\u003c/sup\u003eRa, and \u003csup\u003e232\u003c/sup\u003eTh in the water ranged from 0.35 ± 0.03 to 33.28 ± 2.97 Bq l⁻¹, 1.32 ± 0.07 l⁻¹ to 3.06 ± 0.18 Bq l⁻¹, and 0.85 ± 0.14 to 2.41 ± 0.15 Bq l⁻¹, respectively. For all five age groups, the annual effective dose from radionuclide intake in water sources across the three communities exceeded recommended limits, except in sample locations SW1, SW2, SW3, SW6, PWVI, and PWVII between the ages of 2-7 years and 7-12 years, where the \u003csup\u003e232\u003c/sup\u003eTh level was found to be below the recommended limit of 1.0 mSv y⁻¹ set by ICRP. Therefore, considering that the annual effective dose exceeds the WHO and ICRP recommended guidelines, the water sources from the mining areas should be restricted to domestic use and not for public consumption except after treatment.\u003c/p\u003e","manuscriptTitle":"Natural Radioelements Concentration Analysis and Risk Assessment in Drinking Water of Mining Area Niger State, North Central Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 11:39:56","doi":"10.21203/rs.3.rs-6889551/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6b7da3c-c685-424f-9ddf-de92072040e2","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50022633,"name":"Nuclear Physics"}],"tags":[],"updatedAt":"2025-06-16T11:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 11:39:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6889551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6889551","identity":"rs-6889551","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}