Insights on Climate-Driven Fluctuations of Cave ²²²Rn and CO₂ Concentrations Using Statistical and Wavelet Analyses

Abstract

Understanding the fluctuations in cave air concentrations and their climatic control is substantial not only to preserve the quality of indoor atmospheres but also to avoid the risk related to the presence of hazardous substances. In this study, we investigated the most influential factors affecting 222Rn and CO2 concentrations, the nature of their dynamics, and their coupling with climatic variations. For this purpose, we combined a set of mathematical methods that included a statistical and wavelet analysis of a 6-year time series in Rull Cave (Spain). Generally, the 222Rn and CO2 dynamic in cave air showed similar patterns. However, the obtained results show that these gases have a different frequency response. Thus, the annual component of 222Rn and CO2 is controlled by the relationship between external and internal temperatures. At low frequencies, both gases are affected by the same variables when the cave atmosphere reaches a minimum concentration. However, when the cave atmosphere is isolated from the outdoors, 222Rn and CO2 behave differently and disturbance caused by the visitors is evidenced in terms of the CO2 concentration; the latter observation was confirmed by the wavelet analysis at high frequencies. In contrast, the 222Rn concentration shows important variations following rainfall, which was weakly identified in the CO2 concentration.

Fractal discrete fracture network modeling of radon gas concentration in underground tunnels under Książ Castle in Poland

“…Given the results, seasonal changes observed in the annual cycle are typical for many underground facilities in the world (Barbosa et al 2010vási et al 2010Vaupotič et al 2010;Alvarez-Gallego et al 2015;Gillmore at al. 2005;Groves-Kirby et al 2015;Tchorz-Trzeciakiewicz and Parkitny 2015;Kleinschmidt et al 2018;Smith et al 2019;Pla et al 2020Pla et al , 2023Ambrosino et al 2020;Smetanová et al 2023;Miklayev et al 2024). Daily changes in 222 Rn activity concentrations are not significant but are dependent on the seasonal changes.…”

Land-use changes and precipitation cycles to understand hydrodynamic responses in semiarid Mediterranean karstic watersheds

“…Additionally, a wavelet analysis (Jevrejeva et al, 2003;Grinsted et al, 2004) was carried out to assess the total annual precipitation time series and detect the contribution of the precipitation cycles to the hydrological response and the presence and magnitude of those cycles. Continuous wavelet transforms (CWT), using Morlet as the mother wavelet, was employed to differentiate the different frequencies contained in the analysed precipitation time series (Pla et al, 2020). The distinction of the different frequencies of the signal allows us to differentiate the contribution of these components (in our case, annual or interannual) to the real recorded signal.…”

“…The distinction of the different frequencies of the signal allows us to differentiate the contribution of these components (in our case, annual or interannual) to the real recorded signal. In particular, this analysis was performed using the Environmental Wavelet Tool developed by Pla et al (2020), based on Grinsted et al (2004) Matlab toolbox. Finally, potential evapotranspiration was simulated using the Hargreaves method (Hargreaves et al, 1985;Allen et al, 1998), considering the location of the study area and using the temporal series of maximum and minimum temperature.…”

Does Environmental Pollution hurt Agricultural Productive Efficiency? A New Evidence using ARDL and Wavelet Coherence Approaches

“…However, there has been no sign from existing literature demonstrating a dynamic linkage between climate change, CO2 emissions, and agricultural productivity using the wavelet coherence technique. The wavelet method of analysis gives an in-depth association of determinants within the timescale and frequency bands (Pla et al, 2020). The wavelet efficiency becomes an upsurge when it comes to interacting lead-lag relationship on time series used in non-linear; it simultaneously allows to measure the dynamic linkages and causality (Adebayo and Akinsola, 2020).…”