Abstract

The study concerns the analysis of 220Rn (thoron) recorded in the surface soil in two sites of the Campi Flegrei caldera (Naples, Southern Italy) characterized by phases of volcanic unrest in the seven-year period 1 July 2011–31 December 2017. Thoron comes only from the most surface layer, so the characteristics of its time series are strictly connected to the shallow phenomena, which can also act at a distance from the measuring point in these particular areas. Since we measured 220Rn in parallel with 222Rn (radon), we found that by using the same analysis applied to radon, we obtained interesting information. While knowing the limits of this radioisotope well, we highlight only the particular characteristics of the emissions of thoron in the surface soil. Here, we show that it also shows some clear features found in the radon signal, such as anomalies and signal trends. Consequently, we provide good evidence that, in spite of the very short life of 220Rn compared to 222Rn, both are related to the carrier effect of CO2, which has significantly increased in the last few years within the caldera. The hydrothermal alterations, induced by the increase in temperature and pressure of the caldera system, occur in the surface soils and significantly influence thoron’s power of exhalation from the surface layer. The effects on the surface thoron are reflected in both sites, but with less intensity, the same behavior of 222Rn following the increasing movements and fluctuations of the geophysical and geochemical parameters (CO2 flux, fumarolic tremor, background seismicity, soil deformation). An overall linear correlation was found between the 222−220Rn signals, indicating the effect of the CO2 vector. The overall results represent a significant step forward in the use and interpretation of the thoron signal.

Highlights

  • ObjectivesThe goal of this study is theand demonstration present system

  • The analysis of our recorded 220 Rn time series in soils within the Campi Flegrei caldera area was performed using the sequential application of separate signal processing methods, described in Section 2.2, focused on the recognition of the trends and the possible residuals of the signals [36,45,46,47]

  • The occasional lack of data in the time series was treated with the Self-Consistent Regression Estimator (SCRE) technique that recursively imputes missing values [40]

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Summary

Objectives

The goal of this study is theand demonstration present system

Methods
Results
Conclusion
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