Predicting radon flux density from soil surface using machine learning and GIS data

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany - a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

Responses of underground air and drip water geochemistry to meteorological factors: A multi-parameter approach in the Rull Cave (Spain)

Our research aims to assess the complex interactions between the elements that constitute and influence a cave system through the analysis of an extensive dataset of climatic and environmental parameters (222Rn, CO2, drip rates, chemical composition, and environmental isotopes) measured in air, water, and solid in the Rull Cave (southeastern Spain). Of particular importance is understanding the effect of rainfall and temperature on water and gas transport through the epikarst and the involved processes. Our results show that the cave gaseous concentration patterns do not only depend on the temperature-caused movement of air masses, but they can also be affected by abundant rainfall. The δ18O and δD composition of cave water also relies on such precipitations for the effective transfer of the rainfall signal into the cave, which can take between 3 and 7 days. The elemental ratios (Sr/Ca and Mg/Ca) show high responsiveness to the water drip rate, hinting that enhanced prior calcite precipitation (PCP) occurs at slower drip rates. Despite this, and regardless of drip rates, calcite saturation indices follow a seasonal variation pattern inversely proportional to the cave air CO2 concentration, while δ13C-DIC is proportional. Our results show how the interlinkage between these multiple components defines the dynamics of the atmosphere-soil-cave system. Cave monitoring is then essential to understand the karstic vadose zone, which is highly sensitive to climatic influence and its changes.