RESUMEN
Radon-222, a radioactive noble gas with a half-life of 3.8 days produced by radium-226, is a health hazard in caves, but also a powerful tracer of atmospheric dynamics. Here we show how airborne radon-222 can be analysed in a cave with multiple openings, the Pech Merle Cave in South-West France. This two-level cave hosts prehistoric remains and Gravettian paintings in its lower level. Radon concentration, monitored at 15 points with one-hour sampling intervals for more than one year, including two points for more than three years, showed mean values from 1274 ± 11 to 5281 ± 20 Bq m-3, with transient values above 15,000 Bq m-3. Seasonal variations were observed, with a weak normal cycle (low in winter) at two points in the upper level and a pronounced inverse seasonal cycle (low in summer) at the other points in the cave. The radon-222 source (effective radium-226 concentration, ECRa) was measured in the laboratory for floor deposits, soil and rock samples. While ECRa values obtained for rocks and speleothems are smaller than 1 Bq kg-1, most ECRa values for soils are larger than 10 Bq kg-1. Quantitative modelling confirms that the floor fillings inside the cave are responsible for the stationary lower concentrations, while the higher concentrations observed in winter are explained by percolation of outside air, which collects radon-222 as it passes through the soil layers. In addition, Stored Available Radon (SAR) is sufficient to account for transient variations. While air currents occur when visitors enter the cave or when the cave is deliberately ventilated, the climatic processes revealed by their radon-222 signatures appear to be essentially natural. These processes, enhanced by global climate change, could cause or accelerate the deterioration of prehistoric paintings. Radon-222 source analysis using ECRa-based modelling and SAR appears essential for the preservation of underground heritage.
RESUMEN
The Radon (Rn) deficit technique is a rapid, low-cost, and non-invasive method to identify and quantify light non-aqueous phase liquids (LNAPL) in the soil. LNAPL saturation is typically estimated from Rn deficit using Rn partition coefficients, assuming equilibrium conditions. This work examines the applicability of this method in the presence of local advective fluxes that can be generated by groundwater fluctuations or biodegradation processes in the source zone. To this end, a one-dimensional analytical model was developed to simulate the steady-state diffusive-advective transport of soil gas Rn in the presence of LNAPL. The analytical solution was first validated against an existing numerical model adapted to include advection. Then a series of simulations to study the effect of advection on Rn profiles were carried out. It was found that in high-permeability soils (such as sandy soils), advective phenomena can significantly affect Rn deficit curves in the subsurface compared with those expected, assuming either equilibrium conditions or a diffusion-dominated transport. Namely, in the presence of pressure gradients generated by groundwater fluctuations, applying the traditional Rn deficit technique (assuming equilibrium conditions) can lead to an underestimation of LNAPL saturation. Furthermore, in the presence of methanogenesis processes (e.g., in the case of a fresh LNAPL of petroleum hydrocarbons), local advective fluxes can be expected above the source zone. In such cases, Rn concentrations above the source zone can be higher than those above background areas without advective phenomena, resulting in Rn deficits higher than 1 (i.e., Rn excess), and thus leading to a wrong interpretation regarding the presence of LNAPL in the subsurface if advection is not considered. Overall, the results obtained suggest that advection should be considered in the presence of pressure gradients in the subsurface to ensure an effective application of the soil gas Rn-deficit technique for quantitative estimation of LNAPL saturation.
RESUMEN
A primary goal in the design of reverse osmosis (RO) membranes is to improve water-solute selectivity and water permeance. These transport properties are commonly calculated in the literature using the solution-diffusion model with selectivity (A/B, bar-1) defined as the ratio between water permeance (A, L.m-2.h-1.bar-1) and solute permeance (B, L.m-2.h-1). In calculating transport properties, researchers often use simplifying assumptions about concentration polarization (CP; i.e., assuming negligible CP or a certain extent of CP) and solute rejection (i.e., assuming solute rejection is approximately 1 to enable the explicit use of the CP modulus in solute permeance calculations). Although using these assumptions to calculate transport properties is common practice, we could not find a study that evaluated the errors associated with using them. The uncertainty in these errors could impede unequivocally identifying manufacturing approaches that break through the commonly plotted trade-off frontier between selectivity and water permeance (A/B vs. A); however, we did not find in the literature a study that quantified such errors. Accordingly, we aimed to: (1) quantify the error in transport properties (A, B, and A/B) calculated using common simplifying assumptions about CP and rejection; and (2) determine if using simplifying assumptions affects conclusions drawn about membrane performance or trends concerning the trade-off frontier. Results show that compared with the case where no simplifying assumptions were made, simplified calculations were least accurate at low pressures for water permeance (up to 78% overestimation) and high pressures for solute permeance (up to 188% overestimation). Accordingly, the corresponding selectivities were least accurate at low pressure (up to 111% overestimation) and high pressure (up to 66% underestimation), and conclusions drawn about membrane performance and trade-off trends were pressure-dependent. Importantly, even in the absence of simplifying assumptions, selectivity results were pressure-dependent, indicating the importance of standardizing test conditions for the continued use of current performance metrics (i.e., A/B and A). We propose a two-pressure approach-collecting data for A and B at a high and a low pressure, respectively-combined with simplifying assumptions for more accurate simplified estimations of selectivity (< 10% absolute error). Our work contributes to a better understanding of the effects of operating pressure and key simplifying assumptions commonly used in calculating RO membrane performance metrics and interpretation of corresponding results.