Characterization of seasonal behaviors of naturally occurring radioactive materials in a groundwater borehole using an in situ monitoring system

Understanding the temporal behaviors of naturally occurring radioactive materials is important for safeguarding groundwater as a secure water resource for drinking, agriculture, and industry usage. This study reports the vertical profiles of 238U concentration and 222Rn activity and the management of in situ monitoring systems during intensive field sampling of a national groundwater-monitoring borehole for seven years (2015-2021). The aim was to capture the seasonal characteristics of the 238U concentrations and 222Rn activity. Both factors were low in the rainy season and high in the winter season, reflecting the dilution effect of rainfall recharge. The 238U and 222Rn behaviors were associated with water-rock interactions of calcite dissolution in fracture zones filled with carbonate minerals. Furthermore, multilayer perceptron models estimated the 238U concentration and 222Rn activity with reasonable regression and classification accuracy. Hydrometeorological indicators (temperature and groundwater-level fluctuations) were more important estimators of 238U concentration and 222Rn activity than geochemical process indicators. The regression accuracy performance was higher at deeper sampling depths, where seasonality in the 238U and 222Rn behaviors dominated. From the predicted distributions of 238U concentrations and 222Rn activities, we could estimate the ranges of 238U concentrations and 222Rn activities emerging from groundwater boreholes. High exposure threats from 238U and 222Rn during groundwater usage were found in the winter season. When the multilayer perceptron models use the entire in situ monitoring data at refined temporal resolution, we can quickly determine the naturally occurring radioactive materials and further develop the national groundwater-monitoring borehole equipped with the in-situ monitoring system, supplementing the occasionally obtained field-measurement data.