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1. $$^{222}$$Rn  emanation measurements for the XENON1T experiment

   https://doi.org/10.1140/epjc/s10052-020-08777-z  

   XENON Collaboration ; Aprile, E. ; Aalbers, J. ; Agostini, F. ; Alfonsi, M. ; Althueser, L. ; Amaro, F. D. ; Antochi, V. C. ; Angelino, E. ; Angevaare, J. R. ; et al ( April 2021 , The European Physical Journal C)

   The selection of low-radioactive construction materials is of utmost importance for the success of low-energy rare event search experiments. Besides radioactive contaminants in the bulk, the emanation of radioactive radon atoms from material surfaces attains increasing relevance in the effort to further reduce the background of such experiments. In this work, we present the$$^{222}$$${}^{222}$Rn emanation measurements performed for the XENON1T dark matter experiment. Together with the bulk impurity screening campaign, the results enabled us to select the radio-purest construction materials, targeting a$$^{222}$$${}^{222}$Rn activity concentration of$$10\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$$10\mu \mathrm{Bq}/\mathrm{kg}$in$$3.2\,\mathrm{t}$$$3.2t$of xenon. The knowledge of the distribution of the$$^{222}$$${}^{222}$Rn sources allowed us to selectively eliminate problematic components in the course of the experiment. The predictions from the emanation measurements were compared to data of the$$^{222}$$${}^{222}$Rn activity concentration in XENON1T. The final$$^{222}$$${}^{222}$Rn activity concentration of$$(4.5\pm 0.1)\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$$(4.5\pm 0.1)\mu \mathrm{Bq}/\mathrm{kg}$in the target of XENON1T is the lowest ever achieved in a xenon dark matter experiment.

    

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2. Radon-222 and Radium-226 seawater and ice data from Bering Sea and Chukchi Sea 2021

   https://doi.org/10.18739/A23X83N3N  

   Stephens, Mark ( January 2023 , Arctic Data Center)

   Mercury (Hg) concentrations and speciation within surface waters of the Arctic Ocean are controlled by a complex set of processes including photochemical and microbial transformations, redox reactions, and air-sea exchange of gaseous and particulate Hg species. In this study, our aim was to estimate the magnitude of volatile Hg fluxes across the air-sea interface, and examine the influence of ice cover on this process. While gas exchange in the open ocean has been modeled as a function of wind speed, the parameterization is problematic in the presence of sea ice, which can physically block gas exchange, as well as reduce fetch and dampen waves. By using measurements of Radon-222 (Rn-222) gas and it parent isotope, Radium-226 (Ra-226), to accurately measure gas exchange velocities (k), the relative impacts of chemical and biological processes on mercury distributions within the surface waters can then be deduced. This dataset contains Radon-222 and Radium-226 activity concentrations from R/V Sikuliaq cruise SKQ202108S in the Bering Sea, through the Bering Strait, and in shelf waters of the Chukchi Sea during May – June 2021. Samples include seawater (16 water column profiles), as well as ice cores and brine from four ice stations. At the time of the cruise, sampling locations in the Bering Sea were ice free and gas transfer velocities (k) estimated from Rn-222 deficits (with respect to Ra-226 concentrations) were in general agreement with published parameterizations of k as a function of wind speed. The springtime retreating ice edge was located at 69-70 degrees north latitude in the Chukchi Sea, and sampling locations there were located along the ice edge, in areas of open water, and at sites within the pack ice up to ~10 kilometers (km) from the ice edge. Gas transfer velocities in the marginal ice zone also reflected recent wind histories, with k values generally at the high end of or exceeding those predicted from the wind speed parameterizations. 

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3. Tracing Bank Storage and Hyporheic Exchange Dynamics Using 222 Rn: Virtual and Field Tests and Comparison With Other Tracers

   https://doi.org/10.1029/2020WR028960  

   Liao, Fu ; Cardenas, M. Bayani ; Ferencz, Stephen B. ; Chen, Xiaobing ; Wang, Guangcai ( May 2021 , Water Resources Research)

   Dynamic hydrologic exchange flows in river beds and banks are important for many ecosystem functions throughout river corridors. Here we test whether the exchanges and the associated mixing between a flooding river and groundwater within the river’s bank can be effectively traced by Radon‐222 (²²²Rn), a naturally occurring, inert, radiogenic, and radioactive gas that can be analyzed and monitored in situ. The assessment was done by simulation of groundwater flow and reactive transport of²²²Rn in the bank following a single, relatively rapid (hours long) flood wave and auxiliary field observations of²²²Rn, temperature and total dissolved solids (a surrogate for any ionic conservative tracer). Results illustrate that²²²Rn is more effective than temperature and total dissolved solids in tracing dynamic hyporheic exchange.²²²Rn variations in space and time are larger than the analytical uncertainty of common measurement methods. The individual effects of aquifer hydraulic conductivity, dispersivity, river water²²²Rn concentration, and bank topography were analyzed through sensitivity analysis. Larger hydraulic conductivity and dispersivity, lower²²²Rn concentration in river water relative to groundwater, and gentler bank slopes resulted in a more prominent and traceable²²²Rn signal. The transport and residence time of exchanged water may be estimated and interpreted using reactive transport models such as those implemented here. However, such application is sensitive to fluctuations in river water²²²Rn, requiring it to be well characterized. The assessment provides guidance for using²²²Rn as a tracer for groundwater and surface water interactions in dynamic settings.

    

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4. Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters

   https://doi.org/10.1029/2022WR033870  

   McKenzie, Tristan ; Dulai, Henrietta ; Lee, Jonghyun ; Dimova, Natasha T. ; Santos, Isaac R. ; Zhang, Bo ; Burnett, William ( March 2023 , Water Resources Research)

   Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical budgets worldwide. Radon (²²²Rn) has been widely used as a natural geochemical tracer to quantify SGD, but field measurements are time consuming and costly. Here, we use deep learning to predict coastal seawater radon in SGD‐impacted regions. We hypothesize that deep learning could resolve radon trends and enable preliminary insights with limited field observations of groundwater tracers. Two deep learning models were trained on global coastal seawater radon observations (n = 39,238) with widely available inputs (e.g., salinity, temperature, water depth). The first model used a one‐dimensional convolutional neural network (1D‐CNN‐RNN) framework for site‐specific gap filling and producing short‐term future predictions. A second model applied a fully connected neural network (FCNN) framework to predict radon across geographically and hydrologically diverse settings. Both models can predict observed radon concentrations withr² > 0.76. Specifically, the FCNN model offers a compelling development because synthetic radon tracer data sets can be obtained using only basic water quality and meteorological parameters. This opens opportunities to attain radon data from regions with large data gaps, such as the Global South and other remote locations, allowing for insights that can be used to predict SGD and plan field experiments. Overall, we demonstrate how field‐based measurements combined with big‐data approaches such as deep learning can be utilized to assess radon and potentially SGD beyond local scales.

    

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5. Observation of radon mitigation in MicroBooNE by a liquid argon filtration system

   https://doi.org/10.1088/1748-0221/17/11/P11022  

   Abratenko, P. ; Anthony, J. ; Arellano, L. ; Asaadi, J. ; Ashkenazi, A. ; Balasubramanian, S. ; Baller, B. ; Barnes, C. ; Barr, G. ; Barrow, J. ; et al ( November 2022 , Journal of Instrumentation)

   Abstract The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222 Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222 Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs. 

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