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Article

Analyzing the 222Rn/220Rn Ratio in a Seismic Area: A Reliable Method to Understand the Development of Active Structural Discontinuities in Earthquake Surveillance and Sustainability

by
Nunzia Voltattorni
*,
Andrea Gasparini
,
Daniele Cinti
,
Gianfranco Galli
and
Monia Procesi
Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma 1, 00143 Rome, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10449; https://doi.org/10.3390/su162310449
Submission received: 24 October 2024 / Revised: 22 November 2024 / Accepted: 25 November 2024 / Published: 28 November 2024
(This article belongs to the Special Issue Active Tectonic, Geological Hazard and Seismic Sustainability)
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Abstract

Studies on the individuation of surface and buried faults in seismic areas using geochemical methods can be considered a valid approach for improving sustainability in the risk assessment framework. Appropriate scientific knowledge of structural geology and its evolution pre/during/post seismic events can play a fundamental role in human safety and resilience. The Abruzzo region (central Italy) underwent to a Mw 6.3 seismic event, in April 2009, that interested L’Aquila city (the county seat of the region) and many villages in the surrounding area. A first soil gas survey including radon (222Rn) and thoron (220Rn) measurements was accomplished soon after the main shock, in an area of ~24 km2 a few kilometers away from L’Aquila city. Results highlighted the spatial influence of the active tectonic on gas migration towards the surface. The area was investigated again in spring 2016, both to evaluate the natural degassing during a period without further meaningful earthquakes and to verify the presence of faults supposed after the previous survey results. Comparing data from the two surveys, a variation in the 222Rn/220Rn ratio was observed, suggesting different sources (deep or superficial) of gas degassing strictly correlated with the temporal variation in soil vertical permeability. Furthermore, the results infer a new structural system different from that known in the literature.

1. Introduction

The natural degassing of some gas species along seismic active faults is an expression of fault permeability and porosity [1]. In particular, measurements of radon (222Rn) and thoron (220Rn) emissions are considered a valid geochemical method to recognize and track tectonic activity [2]. 222Rn is an innately radioactive noble gas deriving from 226Ra decay (which derives from the 238U decay series) and has a 3.8-day half-life. 220Rn derives from the 232Th decay series, and its half-life is only 55.6 s: this characteristic is useful to distinguish sectors where soil gas transport is very fast and with the presence of Th-rich mineral outcrops [3]. The diversity of radon and thoron can provide important information about gas transport, considering that thoron activity can only be transported from very short distances (a few cm) in respect to radon activity (≳100 m [4]). According to LaBrecque [5], a relative increase in radon activity with respect to thoron may result from tectonic stress-inducing gas releases that could be an earthquake precursor [6,7,8,9]. For these reasons, the 222Rn/220Rn ratio in soil gas should be monitored since it can be useful for discriminating the radon origin and the processes that can drive its migration. Variations in the 222Rn/220Rn ratio are generally related to endogenous factors such as changes in the underground geological structure, presence of carrier gases and gas source proximity [10]. Furthermore, this ratio varies as a result of the 226Ra/222Rn and 224Ra/220Rn reaction-time periods to reach equilibrium.
The investigated area is part of an intermountain tectonic basin belonging to the central Apennine fold-and-thrust belt. The basin formed in the Quaternary due to the post-orogenic extensional tectonic activity developed in the previous compressional structures in the NW–SE direction and generating active normal faults [11,12,13,14]. Among these faults, the known structural discontinuities of Paganica, Bazzano and Fossa (Figure 1) are considered responsible for the Mw 6.3 event that affected the area of L’Aquila (Central Italy) in April 2009 [15,16,17]. A first campaign of soil gas measurements was performed within the tectonic basin immediately after the main shock. During that study, Voltattorni et al. [18] highlighted and interpreted the 222Rn–CO2 anomaly as the typical geochemical couple where carbon dioxide acts as carrier gas for radon rising. Further, the existence of the 222Rn and CO2 couple suggested an active rock rupture and the probable presence of unknown faults without any surface expression. In the central part of the investigated area (between the Paganica and Bazzano faults, Figure 1), N–S-oriented 222Rn soil gas anomalous distribution was detected and explained as a consequence of superficial rock fracturing and structural discontinuities which were not visible at the surface and/or to minor/transverse faults activated during the long seismic activity of the L’Aquila area.
In this work, we present and discuss the results relative to a second soil gas prospecting performed in 2016 (in absence of notable seismic events, seven years after the Mw 6.3 L’Aquila earthquake) compared with the 2009 soil gas survey. The main aim of this research was to improve the knowledge of faulting spatial distribution controlling radon emissions in this area and to highlight tectonic structures not always visible soon after a major earthquake. Moreover, measuring both radon isotopes (222Rn and 220Rn) at the same sampling points has enabled us to improve the comprehension between radon anomalies and seismotectonic.
Our results are aimed to both highlight the importance of evaluating the spatial distribution and extension of faults during aseismic periods when natural degassing reveals gas migration pathways (faults and/or fractures) and to contribute to the improvement of the risk assessment in the frame of earthquake monitoring and sustainability of human practices in the territory.

2. Materials and Methods

The surveyed area covers 24 km2 and has been studied by means of 135 soil gas collection and measurements distributed over an irregular grid (Figure 1). Soil radon investigations can be affected by the interaction of several factors such as geologic and meteorologic factors through time. Field measurements are usually carried out within a few days or at least weeks, so measurements are not affected by seasonal variations. Soil moisture can be one of the most significant factors in the variability of soil radon concentration data, but, during our campaigns, it was minimized by conducting the survey during periods characterized by stable temperature and soil humidity.
Soil 222Rn and 220Rn measurements were carried out using a RAD7 (Durridge® Company, Inc., Billerica, MA, USA) certified alpha spectrometry instrument, commonly used for geochemical investigations (e.g., [19,20,21] and references therein). The RAD7 measured the concentrations of the two isotopes 222Rn and 220Rn in the gas phase by electrostatic collection on a solid state detector of their respective first daughter nuclides 218Po (t1/2 = 3.04 min) and 216Po (t1/2 = 0.145 s), positively charged soon after their creation, and detection of the emitted alpha particles. Fast measurements were carried out in sniff-mode, during which only alpha decays from 218Po were counted; after 5 min intervals, temporary radon concentrations were recorded. The time necessary for 218Po and 222Rn nuclei to reach equilibrium is about 5 times the half-life of 218Po, and, for this reason, accurate measurements need at least a 15 min period. The gas was pumped from a steel probe inserted at a depth of at least 0.50 m into the soil. A desiccant trap (calcium sulphate, also known as drierite) together with an inlet filter was used to protect the detector from soil dirt and humidity (which should preferably be below 10%). The measurement range was 4–750,000 Bq/m3; the typical sensitivity in sniff-mode (only 218Po decays were analyzed) was 0.0067 cpm/(Bq/m³). For 5 min cycles, in sniff-mode, 1 count corresponded to around 30 Bq/m3 with an uncertainty of around 150 Bq/m3 at a 95% confidence level.
A radon chamber was regularly used to calibrate the used RAD7 [22]. Soil gas samples were collected using a 1 m long stainless steel probe (with a conical point at the bottom) that as inserted to a minimum depth of 0.5 m in the soil, sufficiently deep to avoid atmospheric air influencing soil gas concentrations [23]. A syringe was used, first to clean the probe and then to sample soil gases by means of manual aspiration. The extracted gases were stored in small prevacuumed steel containers (25 mL) that would be analyzed at the laboratory. A microGC Agilent 4900 (© Agilent Technologies, Inc., Santa Clara, CA, USA) was used for gas analysis (i.e., O2, N2, H2 and He using a Molsieve–TCD column and CH4, CO2 and H2S using a PoraPlot Q column). Certified gases were utilized for instrumental calibration. The gas carrier for both the columns (analytical error < 5%) was argon. Since the first survey was performed in April 2009, the 2016 soil gas survey was accomplished during the same seasonal period, in order to find similar meteorological conditions to the previous survey.
All obtained data were statistically analyzed using both common (median, mean, standard deviation) and peculiar (kurtosis, skewness) statistical parameters necessary to figure out 220Rn and 222Rn soil gas anomalous concentrations that would reveal phenomena such as gas migration from a deep source, e.g., [24,25,26]. Graphical processing was employed to characterize geochemical anomalies and lineaments. Classed post maps were elaborated upon after the recognition of threshold values [27] on NPPs (Normal Probability Plots): the identification of particular population data (i.e., background, anomalies, outliers) let us to define the spatial distribution of anomalous values.
Variogram surface analysis was applied for a geostatistical approach of soil gas distribution in order to process the assessment of the spatial continuity of gas concentration anomalies, to accomplish useful parameters for the construction of a contour map and, therefore, to identify possible fault-related anisotropy.

3. Results

The results from statistical analysis of soil gases are shown in Table 1. Besides basic statistical parameters, other indexes like the skewness and the interquartile range (IQR) were also considered, as they can help to check the normal distribution of the dataset. If skewness values do not equal zero, then the distribution is not normal and outliers occur (i.e., values much higher than the mean). The IQR, which includes 50% of cases, is the width of the interval around the median, and thus it provides a measure of data dispersion. Since He, H2, Ne, CO2, CH4, O2 and N2 gas concentrations are mostly consistent with previous data (Table 1) reported in Voltattorni et al. [18], they will not be discussed in the present manuscript.
During the 2009 survey, 222Rn and 220Rn values varied from 0.2 to 38.9 kBq/m3 and from 0 to 133.0 kBq/m3, respectively. Radon had a high dispersed distribution, as highlighted by the high values of standard deviation (S.D., 7.4 kBq/m3) and skewness (1.3), indicating the presence of anomalous values (outliers). During the 2016 survey, 222Rn and 220Rn values ranged from 0.1 to 49.0 kBq/m3 and from 0.1 to 341.0 kBq/m3, respectively. Radon concentrations as well as statistical parameters were very similar in the two surveys, although 220Rn median value, standard deviation and skewness were surprisingly much higher in the 2016 survey, indicating the occurrence of very anomalous concentrations (i.e., outliers).
The t-test [28] was applied to 222Rn and 220Rn datasets in order to categorize high concentrations as “outliers” or “extreme” values, as shown by the box plots (Figure 2). It is well evident that in the 2016 survey, 220Rn had higher concentrations (median value 41 kBq/m3), higher outliers (from 139 to 189 kBq/m3) and higher extreme values (from 205 to 341 kBq/m3) compared to the 2009 survey. On the other hand, 222Rn concentrations are similar in the two surveys. Covariance among 222Rn and 220Rn was evaluated statistically (Figure 3): correlation values are very similar in the two surveys (r = 0.4) and are typical of active geodynamical contexts, e.g., [29,30]. Anomaly thresholds were defined using Normal Probability Plots (NPPs) interpreted according to Sinclair [27]: anomalous populations were identified for both 222Rn (values > 24 KBq/m3) and 220Rn (values > 70 kBq/m3).

4. Discussion

According to Walters et al. [31], for hazard assessment in the L’Aquila area, all faults, beyond those already known, that are close to failure due to stress changes caused by the 2009 earthquake should be identified. Soon after the beginning of the earthquake sequence, many fractures opened, causing a strong natural degassing of the soil.
Once the earthquake sequence finished, some structures started to close (due to land settlement), blocking gas ascending. Consequently, soil gas concentrations were dissipated into the air, allowing the system to return to an equilibrium state. Nevertheless, soil gas fluxes keep going where the opening of fractures and faults persists at depth. The variations in soil radon and thoron concentrations can enhance the comprehension of the source (deep or shallow) of rare soil gases. The source discrimination would allow the individuation of active faults during aseismic periods due to deep origin gas migration along them. However, it is noteworthy to underline that gases can migrate preferentially through fractured zones but only along pathways whose permeability has been enhanced by seismic activity. Lombardi and Voltattorni [32] investigated the Campidano graben (Sardinia Island, Italy) characterized by seismic quiescence and an almost complete lack of historical earthquakes. They constantly observed low gas values because of sealed, non-active faults that prevent gas migration.
Since CO2 is considered the main carrier gas for rapid transport of radon isotopes from long distances ([33,34,35] and references therein), 222Rn/220Rn values are plotted against carbon dioxide (Figure 4) in order to characterize radon and thoron sources. The good correlation between high 222Rn and 220Rn values is evident in Figure 4, except for a few small thoron concentrations (to the right, at the bottom of the plot) without meaningful radon values. In this latter case, we can infer a superficial gas source that can produce scarce concentrations of radon due both to dilution by atmospheric air and the soil depressurization caused by small fractures before or soon after seismic events. Radon concentrations are, therefore, not increased by these small quantities of gas because of its original relative high background level. On the other hand, thoron is more responsive to these superficial sources, and its concentrations will be significantly improved during shallow and superficial seismic events.
Figure 5 and Figure 6 show a comparison of radon and thoron (respectively) contour maps between gas concentrations soon after the 2009 earthquake and during the aseismic period (2016). During the first campaign, the spatial concentrations of both 222Rn and 220Rn soil gas anomalous values were extensively distributed all over the study area, with the exclusions of some spots where radon and thoron activities were very low. According to Voltattorni et al. [18], radon and thoron spatial spread is due to intense shallow fracturing and to structural discontinuities which are not evident at the surface or to minor/transverse faults activated during the long seismic activity of the L’Aquila area. The third-order polynomial regression of 222Rn activity during the two campaigns is plotted against the AB profile parallel to the geological cross section by Guerrieri et al. [36] intersecting the two main faults (Bazzano and Paganica faults) and one minor fault (Colle Caticchio fault). Radon values show different trends along the three faults: very low during the seismic sequence (confirming the gas dissipating due to the stress regime) and higher during the 2016 survey where 222Rn anomalies occur as spots near the known faults. However, 222Rn outlines a preferential migration direction in the area among the Colle Caticchio and Paganica faults (green rectangle in Figure 5), suggesting the presence of one new migration pathway that probably was activated during the seismic event and that has remained opened, as highlighted by the high radon values during both surveys. The variation in 222Rn distribution during the two surveys is evident: during and soon after the seismic events, a widespread gas leakage occurs. Thereafter, the radon flux is more stable and localized in areas where the fracture opening persists at depth.
According to Yang et al. [37], when low 222Rn and 220Rn values are associated, the gas source should be shallow because both the thoron migration is mainly diffusive from the soil superficial substratum characterized by rocks containing 232Th and the gas migration is encouraged by soil microfractures. Conversely, when high 222Rn and 220Rn values are associated, the correlation is affected by physical agents (e.g., wetness, soil porosity, velocity of gas spread, depth of the gas source, hidden fractures, etc.) that favor gas migration. Figure 7 shows classed post maps of 222Rn/220Rn ratio both during seismic (to the left) and quiescent periods (to the right). Widespread anomalies (222Rn/220Rn > 0.15) in 2009 are due to high 222Rn values, whose migration was favored by high permeability and porosity of soil during the seismic sequence.
On the contrary, the rare and aligned spot anomalies (222Rn/220Rn > 0.3) in 2016 would confirm that seismic events caused, at depth, a permanent fracture system that facilitated radon migration towards the soil surface. To better understand the spotted distribution of anomalous radon values, an experimental variogram has been elaborated upon to estimate the spatial continuity (in terms of geometry) of high 222Rn/220Rn values of the latter campaign. Figure 8 shows the variogram model fitted to the computed data and calculated along the axes of anisotropy (132°) and an angular tolerance of 28°. The x-axis shows the lag distance (distances between pairs at which the variogram is calculated), and the y-axis shows the equation for computing the variogram).
The fitting model is the exponential function where sample couples are autocorrelated up to the distance of almost 1800 m (after that, the variogram achieves the asymptotic threshold value of 66 γ). The elaborated variogram has been used in the kriging calculation for a better evaluation of 222Rn/220Rn ratio distribution map (Figure 8). The maximum anisotropy has a NW–SE orientation that is typical of the Apennine trend belt characterized by active faults that caused ancient and recent earthquakes [38]. The results of the variogram map would confirm that high soil gas concentrations occur along elongated zones, probably correlated to the strain change, especially because the identified anisotropy has the same direction of regional faults responsible for the structural extensional process [37]. In summary, the variation and increase in the extensional stress can result in promoting new faults that can be detectable in analyzing radon gas flows.
In the frame of seismic hazard assessment, our results indicate that the application of the soil gas technique in a seismic area is of great practical significance for studying the activity of faults since the spatiotemporal distribution of radon concentration is sensitive to changes in stress/strain related to seismic activity. There are many different geochemical methods for studying faults that are focused on the individuation of special patterns [39] of element migration and enrichment (for instance, Na, K and Ca are commonly lost from the fault zone, while volatile components—H2O and CO2—are prone to be enriched within the fault zone), or on hydrothermal fluid properties (e.g., isotope chemistry) [40]. These methods are very efficient for the study of active faults but not relevant for both the blind fault research and the study of variation in soil permeability (due to stress regime variation) to the gas uprising. The results of soil gas 222Rn concentrations from the investigated area are similar to values of active faults reported in the recent literature ([41,42,43,44,45,46] and references therein). The great potential of radon as an earthquake precursor or as a fault tracer is accepted worldwide. On the contrary, 220Rn is rarely studied, and its role as a radon source indicator is often underestimated. For this reason, the achieved results obtained by studying the 222Rn/220Rn ratio provide a reliable tool for the spatial domain quantification of unknown structural discontinuities that have no clear expression at surface.

5. Conclusions

Every earthquake event can have a great socio-economic impact, and, for this reason, it is crucial to improve the knowledge of the territory for seismic reduction plans beside appropriate building technologies. The characterization of the near-surface soil gas permeability (enhanced by migration pathways) can be an accurate and effective instrument for identifying the spatial distribution of the fault system. This study has confirmed the presence of high degassing structures (known faults) at the L’Aquila seismic area and, at the same time, revealed new fault strands, probably a complex network of smaller or blind faults that should not be underestimated in the evaluation of the seismic hazards of the area. In particular, the 222Rn/220Rn ratio has provided evidence of vertical fluid flow due to the existence of faults that locally chance the porosity of soil and allow the gas to move in a more permeable contest. The obtained results suggest that post-earthquake field investigation, during aseismic periods, should be accomplished in order to highlight secondary tectonic structures not always visible soon after a major earthquake. Together with the recognition of major and secondary faults, it is necessary to undertake extended periods of regular and continuous monitoring of radon gas emissions at one or more selected locations, to better assess the possible development of the seismogenic process that can intensify the releasing of endogenous fluids. On the issue as a whole, the statistical analysis of compositional data in relation to the observed seismic activity is also essential. This approach would allow us to define the potential nearby seismic sources, which may possibly contribute to the seismic surveillance and sustainability.

Author Contributions

Conceptualization, N.V.; methodology, N.V. and G.G.; validation, N.V., A.G., D.C., G.G. and M.P.; formal analysis, N.V.; investigation, N.V., A.G., D.C. and G.G.; data curation, N.V.; writing—original draft preparation, N.V., A.G., D.C., G.G. and M.P.; writing—review and editing, N.V., A.G., D.C., G.G. and M.P.; project administration, N.V.; funding acquisition, N.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Istituto Nazionale di Geofisica e Vulcanologia—Struttura Ambiente-Linea di Attività A6: Monitoraggio Ambientale, Sicurezza e Territorio. This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation to any qualified researcher.

Acknowledgments

The authors would like to thank L. Piccolini, whose help and presence during field work was highly appreciated. Thanks to the Section Managing Editor and the Assistant Editor for their kindness and availability during the submission process. The authors dearly thank two anonymous reviewers for their constructive comments, which have contributed to improving the quality of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Simplified geo-structural map of the studied area showing the main faults (red lines) and the sites of soil gas measurements (black dots).
Figure 1. Simplified geo-structural map of the studied area showing the main faults (red lines) and the sites of soil gas measurements (black dots).
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Figure 2. The box plots show a comparison of 222Rn and 220Rn values obtained from both surveys. The boxes were plotted using lower and upper quartiles (edges of the box), the median (line and number at the center of the box), whiskers (defined by the IQR factor) and outliers (blue/red dots).
Figure 2. The box plots show a comparison of 222Rn and 220Rn values obtained from both surveys. The boxes were plotted using lower and upper quartiles (edges of the box), the median (line and number at the center of the box), whiskers (defined by the IQR factor) and outliers (blue/red dots).
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Figure 3. Statistical comparison of 222Rn and 220Rn mean values from both surveys using t-test.
Figure 3. Statistical comparison of 222Rn and 220Rn mean values from both surveys using t-test.
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Figure 4. 222Rn/220Rn values from both surveys (2009 and 2016) versus CO2. Anomalous high 222Rn and 220Rn peaks have a good correlation, except for a few 2009 thoron values, probably due to a shallow gas source.
Figure 4. 222Rn/220Rn values from both surveys (2009 and 2016) versus CO2. Anomalous high 222Rn and 220Rn peaks have a good correlation, except for a few 2009 thoron values, probably due to a shallow gas source.
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Figure 5. Comparison of 222Rn contour maps between gas concentrations soon after the 2009 earthquake and during the aseismic period (2016). The 3rd-order polynomial regression of 222Rn activities was plotted against the AB profile parallel to the geological cross section by Guerrieri et al. [36].
Figure 5. Comparison of 222Rn contour maps between gas concentrations soon after the 2009 earthquake and during the aseismic period (2016). The 3rd-order polynomial regression of 222Rn activities was plotted against the AB profile parallel to the geological cross section by Guerrieri et al. [36].
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Figure 6. A comparison of 220Rn contour maps between gas concentrations soon after the 2009 earthquake and during the aseismic period (2016). The 3rd-order polynomial regression of 222Rn activities was plotted against the AB profile parallel to the geological cross section by Guerrieri et al. [36].
Figure 6. A comparison of 220Rn contour maps between gas concentrations soon after the 2009 earthquake and during the aseismic period (2016). The 3rd-order polynomial regression of 222Rn activities was plotted against the AB profile parallel to the geological cross section by Guerrieri et al. [36].
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Figure 7. Classed post maps of 222Rn/220Rn ratio during both seismic (to the left) and quiescent periods (to the right). Widespread anomalies (222Rn/220Rn > 0.15) in 2009 are due to high 222Rn values of gas migration favored by high permeability and porosity of soil during the seismic sequence.
Figure 7. Classed post maps of 222Rn/220Rn ratio during both seismic (to the left) and quiescent periods (to the right). Widespread anomalies (222Rn/220Rn > 0.15) in 2009 are due to high 222Rn values of gas migration favored by high permeability and porosity of soil during the seismic sequence.
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Figure 8. An experimental variogram (to the left) and the relative radon fitting model (to the right) determined along the axes of anisotropy (132°) and an angular tolerance of 28°. The x-axis represents the lag distance (distances between pairs at which the variogram is calculated), and the y-axis refers to the equation for computing the variogram and depends on the variance of the variable in the function of the lag distance.
Figure 8. An experimental variogram (to the left) and the relative radon fitting model (to the right) determined along the axes of anisotropy (132°) and an angular tolerance of 28°. The x-axis represents the lag distance (distances between pairs at which the variogram is calculated), and the y-axis refers to the equation for computing the variogram and depends on the variance of the variable in the function of the lag distance.
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Table 1. Descriptive statistical results of soil gases from the two surveys: in 2009 soon after the L’Aquila earthquake and in 2016.
Table 1. Descriptive statistical results of soil gases from the two surveys: in 2009 soon after the L’Aquila earthquake and in 2016.
MeanMedianMin ValueMax ValueS.D.IQRSkewnessAnomaly Threshold
222Rn_2009 (Bq/m3)8900670023838,9007.48.91.31440
222Rn_2016 (Bq/m3)9000760010049,0007.88.02.124,300
220Rn_2009 (Bq/m3)25,80019,5000133,00025.030.31.552,000
220Rn_2016 (Bq/m3)51,60041,00063341,00049.441.32.571,400
He_2009 (ppm, v/v)5.04.90.510.31.70.43.45.3
He_2016 (ppm, v/v)5.35.00.424.62.50.72.75.2
H2_2009 (ppm, v/v)0.80.50.25.70.81.25.60.6
H2_2016 (ppm, v/v)1.10.80.216.51.22.56.30.7
O2_2009 (%, v/v)18.919.018.018.80.70.03−1.719
O2_2016 (%, v/v)18.018.119.919.70.50.03−1.718.9
N2_2009 (%, v/v)76.276.374.378.50.90.01−0.278.2
N2_2016 (%, v/v)76.876.874.279.00.60.009−0.278.4
CH4_2009 (ppm, v/v)12.00.50.2642.562.45.18.71.4
CH4_2016 (ppm, v/v)9.83.90.1543.0495.110.71.3
CO2_2009 (%, v/v)2.62.20.18.21.70.61.11.2
CO2_2016 (%, v/v)0.80.70.12.60.50.71.11.0
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Voltattorni, N.; Gasparini, A.; Cinti, D.; Galli, G.; Procesi, M. Analyzing the 222Rn/220Rn Ratio in a Seismic Area: A Reliable Method to Understand the Development of Active Structural Discontinuities in Earthquake Surveillance and Sustainability. Sustainability 2024, 16, 10449. https://doi.org/10.3390/su162310449

AMA Style

Voltattorni N, Gasparini A, Cinti D, Galli G, Procesi M. Analyzing the 222Rn/220Rn Ratio in a Seismic Area: A Reliable Method to Understand the Development of Active Structural Discontinuities in Earthquake Surveillance and Sustainability. Sustainability. 2024; 16(23):10449. https://doi.org/10.3390/su162310449

Chicago/Turabian Style

Voltattorni, Nunzia, Andrea Gasparini, Daniele Cinti, Gianfranco Galli, and Monia Procesi. 2024. "Analyzing the 222Rn/220Rn Ratio in a Seismic Area: A Reliable Method to Understand the Development of Active Structural Discontinuities in Earthquake Surveillance and Sustainability" Sustainability 16, no. 23: 10449. https://doi.org/10.3390/su162310449

APA Style

Voltattorni, N., Gasparini, A., Cinti, D., Galli, G., & Procesi, M. (2024). Analyzing the 222Rn/220Rn Ratio in a Seismic Area: A Reliable Method to Understand the Development of Active Structural Discontinuities in Earthquake Surveillance and Sustainability. Sustainability, 16(23), 10449. https://doi.org/10.3390/su162310449

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