Method development for rapid quantification of Rn-222 in surface water and groundwater

Environmental Geochemistry and Health(2019)

引用 3|浏览16
暂无评分
摘要
Understanding the risks of a developing unconventional hydrocarbons industry, including shale gas, to the chemical quality of surface water and groundwater involves firstly establishing baseline compositions against which any future changes can be assessed. Contaminants of geogenic origin are of particular interest and radon has been identified as one potential contaminant from shale sources. Robust measurement and monitoring of radon in water at environmental concentrations is essential for ensuring protection of water sources and maintaining public confidence. Traditional techniques for Rn-222 determination in water, such as inference by gamma spectrometry and direct alpha counting, are impractical for direct field measurement, and the relatively short half-life of Rn-222 (~ 3.82 days) means that longer analytical protocols from field to the laboratory may result in greater uncertainty for Rn-222 activity. Therefore, a rapid and low-cost method would be beneficial. We have developed and refined a laboratory procedure for Rn-222 monitoring using liquid scintillation counting (LSC). The accuracy of Rn-222 activities obtained via this procedure was evaluated by the analysis of almost 200 water samples collected from streams and boreholes as part of a detailed baseline investigation in the Vale of Pickering, Yorkshire, one potential location for future shale gas exploration. LSC was preferred for measurement of Rn-222 and had comparable accuracy to gamma spectrometry and direct alpha counting. The methodology provided a rapid, portable and low-maintenance option relative to the two established techniques and is shown to be a favourable choice for the measurement of radon in surface water and groundwater at environmental concentrations.
更多
查看译文
关键词
Radon, Groundwater, Health, Radioactivity, NORM, Liquid scintillation counting, Triathler
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要