| Home | [1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–30] |
|
Silva, M. L. da, & Bonotto, D. M. (2015). Uranium isotopes in groundwater occurring at Amazonas State, Brazil. Applied Radiation and Isotopes, 97, 24–33.
Abstract: This paper reports the behavior of the dissolved U-isotopes 238U and 234U in groundwater providing from 15 cities in Amazonas State, Brazil. The isotope dilution technique accompanied by alpha spectrometry were utilized for acquiring the U content and 234U/238U activity ratio (AR) data, 0.01–1.4µgL−1 and 1.0–3.5, respectively. These results suggest that the water is circulating in a reducing environment and leaching strata containing minerals with low uranium concentration. A tendency to increasing ARs values following the groundwater flow direction is identified in Manaus city. The AR also increases according to the SW–NE directions: Uarini→Tefé; Manacapuru→Manaus; Presidente Figueiredo→São Sebastião do Uatumã; and Boa Vista do Ramos→Parintins. Such trends are possibly related to several factors, among them the increasing acid character of the waters. The waters analyzed are used for human consumption and the highest dissolved U content is much lower than the maximum established by the World Health Organization. Therefore, in view of this radiological parameter they can be used for drinking purposes.
|
|
Botha, R., Lindsay, R., Newman, R. T., Maleka, P. P., & Chimba, G. (2019). Radon in groundwater baseline study prior to unconventional shale gas development and hydraulic fracturing in the Karoo Basin (South Africa). Applied Radiation and Isotopes, 147, 7–13.
Abstract: The prospect of unconventional shale gas development in the semi-arid Karoo Basin (South Africa) has created the prerequisite to temporally characterise the natural radioactivity in associated groundwater which is solely depended on for drinking and agriculture purposes. Radon (222Rn) was the primary natural radionuclide of interest in this study; however, supplementary radium (226Ra and 228Ra) in-water measurements were also conducted. A total of 53 aquifers spanning three provinces were studied during three separate measurement campaigns from 2014 to 2016. The Karoo Basin’s natural radon-in-water levels can be characterised by a minimum of 1 ± 1 Bq/L (consistent with zero or below LLD), a maximum of 183 ± 18 Bq/L and mean of 41 ± 5 Bq/L. The mean radon-in-water levels for shallow aquifers were systematically higher (55 ± 10 Bq/L) compared to deep (14 ± 3 Bq/L) or mixed aquifers (20 ± 6 Bq/L). Radon-in-water activity concentration fluctuations were predominantly observed from shallow aquifers compared to the generally steady levels of deep aquifers. A collective seasonal mean radon-in-water levels increase from the winter of 2014 (44 ± 8 Bq/L) to winter of 2016 (61 ± 16 Bq/L) was noticed which could be related to the extreme national drought experienced in 2015. Radium-in-water (228Ra and 226Ra) levels ranged from below detection level to a maximum of 0.008 Bq/L (226Ra) and 0.015 Bq/L (228Ra). The 228Ra/226Ra ratio was characterised by a minimum of 0.93, a maximum of 6.5 and a mean value of 3.3 ± 1.3. Developing and improving baseline naturally occurring radionuclide groundwater databases is vital to study potential radiological environmental impacts attributed to industrial processes such as hydraulic fracturing or mining.
|
|
Romeo, N., Mabry, J., Hillegonds, D., Kainz, G., Jaklitsch, M., & Matsumoto, T. (2022). Developments of a field gas extraction device and krypton purification system for groundwater radio-krypton dating at the IAEA. Applied Radiation and Isotopes, 189, 110450.
Abstract: The long-lived radio-krypton isotope 81Kr (t1/2 = 2.29 × 105 yr) is an ideal tracer for old groundwater age dating in the range of 105–106 years which goes beyond the reach of radio-carbon (14C) age dating. Analytical breakthrough made over the last two decades in Atom Trap Trace Analysis (ATTA) has enabled the use of this isotope with extremely low abundance (81Kr/Kr = 6 × 10−13) to be used as a practical dating tool for very old groundwater. The International Atomic Energy Agency aims to provide this new isotope tool for better groundwater resource management of Member States and developed a field sampling device to collect dissolved gas samples from groundwater and a system to separate and purify trace amounts of krypton from the gas samples for the ATTA analysis. The design, setup and performances of our sampling and purification systems are described here. Our system can produce a high purity aliquot of about 5 μL of krypton from 5 L of air sample (recovery yield of >90%). The samples made by our system were confirmed to be acceptable for the ATTA analysis.
|
|
Haque, N., & Norgate, T. (2014). The greenhouse gas footprint of in-situ leaching of uranium, gold and copper in Australia. Journal of Cleaner Production, 84, 382–390.
Abstract: In-situ leaching (ISL) is a chemical method for recovering useful minerals and metals directly from underground ore bodies which is also referred to as ‘solution mining’. ISL is commonly used for uranium mining, accounting for about 45% of global production. The main benefits are claimed to be a lower environmental impact in terms of visual disturbances, emissions, lower energy use, cost compared with conventional open-cut or underground mining methods, and potential utilisation of lower grade resources. However, there is a lack of reported studies on the assessment of the environmental impacts of ISL, particularly greenhouse gas (GHG) emissions using life cycle assessment (LCA) methodology. The SimaPro LCA software was used to estimate the GHG footprint of the ISL of uranium, gold and copper. The total GHG emissions were estimated to be 38.0 kg CO2-e/kg U3O8 concentrate (yellowcake), 29 t CO2-e/kg gold, and 4.78 kg CO2-e/kg Cu. The GHG footprint of ISL uranium was significantly lower than that of conventional mining, however, the footprints of copper and gold were not much less compared with conventional mining methods. This is due to the lower ore grade of ISL deposits and recovery compared with high ore grades and recovery of conventional technology. Additionally, the use of large amount of electricity for pumping in case of ISL contributes to this result. The electricity consumed in pumping leaching solutions was by far the greatest contributor to the well-field related activities associated with ISL of uranium, gold and copper. The main strategy to reduce the GHG footprint of ISL mining should be to use electricity derived from low emission sources. In particular, renewable sources such as solar would be suitable for ISL as these operations are typically in remote locations with smaller deposits compared with conventional mining sites.
|
|
Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: Automated machine learning model for the prediction of groundwater level. Engineering Applications of Artificial Intelligence, 127, 107405.
Abstract: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.
|