Statistical analysis reveals spatial and temporal anomalies of soil CO2 efflux on Mount Etna volcano (Italy)

Journal of Volcanology and Geothermal Research 194 (2010) 1–14

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Journal of Volcanology and Geothermal Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j vo l g e o r e s

Statistical analysis reveals spatial and temporal anomalies of soil CO2 efflux on Mount Etna volcano (Italy) Salvatore Giammanco a,⁎, Fernando Bellotti b,c, Gianluca Groppelli c, Annamaria Pinton b a b c

Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Piazza Roma 2, 95123 Catania, Italy Dipartimento di Scienze della Terra “A. Desio”, Via Mangiagalli 34, 20133 Milano, Italy Istituto per la Dinamica dei Processi Ambientali, sezione di Milano, Consiglio Nazionale delle Ricerche, Via Mangiagalli 34, 20133 Milano, Italy

a r t i c l e

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Article history: Received 4 November 2009 Accepted 5 April 2010 Available online 18 April 2010 Keywords: Mt. Etna soil CO2 effluxes magmatic degassing statistical analysis volcano-tectonic structures

a b s t r a c t Extensive geochemical surveys were carried out on the Western flank of Mt. Etna volcano for the determination of soil CO2 effluxes, in order to study the relationship between soil gas anomalies, faults and volcanic activity. The areas of Santa Maria di Licodia (SML) and W-Rift (WR) were selected, because of their importance within the volcano-tectonic framework of Etna. Two gas surveys were performed in each area in different periods (November 2005 and May 2006 in SML, September 2007 and June 2008 in WR), with 2140 measurements in total. In each survey, data were log-normally distributed and were statistically different from the other surveys, therefore their standard normal form was used to compare them. Log probability plots revealed five populations of data in each survey, due to varying degrees of mixing between biogenic and magmatic CO2, and indicated anomalous CO2 effluxes for values N36 g m−2 d−1. Magmatic output was 39.2 t d−1 in November 2005, 15.8 t d−1 in May 2006, 98.4 t d−1 in September 2007 and 234.1 t d−1 in June 2008. Natural Neighbor interpolation of standardized data produced distribution maps that showed some clustering of anomalous values along directions possibly related to hidden faults compatible with volcanic or regional structural trends. Analysis of magmatic CO2 emissions in time suggested a possible influence from seasonal variations, but comparison with volcanic activity of Etna also indicated a volcanic influence accompanying the 2008–2009 flank eruption. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Since the early work of Irwin and Barnes (1980), it has become clear that there is a close relationship between active tectonic areas and anomalous crustal emissions of carbon dioxide. Due to their high crustal permeability, faults act as preferential pathways for the upward migration and eventual release of deep gases into the atmosphere. This is even more evident in active volcanic areas, especially at Mount Etna (e.g. Aiuppa et al., 2004). Huge summit and flank emissions of CO2 take place at Mt. Etna, whose magnitude has recently been assessed (Allard et al., 1991; D'Alessandro et al., 1997; Giammanco et al., 2007). Flank emissions of carbon dioxide on Mt. Etna occur both as focused and as diffuse degassing. The former takes place in fumaroles, mofettes and mud volcanoes in several sites located mostly close to the volcano summit and on its boundary (Giammanco et al., 1998b, 1999; Pecoraino and Giammanco, 2005; Giammanco et al., 2007), whereas the latter occurs mainly in the central part of Etna's lower eastern and southwestern slopes, and along the many faults that traverse the volcanic pile ⁎ Corresponding author. Fax: + 39 095 435801. E-mail addresses: [email protected] (S. Giammanco), [email protected] (F. Bellotti), [email protected] (G. Groppelli), [email protected] (A. Pinton). 0377-0273/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jvolgeores.2010.04.006

(D'Alessandro et al., 1992; Giammanco et al., 1993, 1995; Allard et al., 1997; Giammanco et al., 1997; Azzaro et al., 1998; Giammanco et al., 1998a, 1999; Aiuppa et al., 2004; Giammanco and Bonfanti, 2009). An important role on the release of CO2 to the surface is played by buried faults, that have no surface evidence because they were covered either by recent volcanic products or by recent alluvial deposits or talus (Giammanco et al., 1997, Azzaro et al., 1998; Giammanco et al., 1998a, 1999; Giammanco and Bonfanti, 2009). The use of soil CO2 surveys has, therefore, proven extremely useful in the detection of buried faults. Mapping these faults by means of soil gas surveys can help better define the structural framework of the Mt. Etna area, particularly where tectonic processes are active but do not have a clear surface evidence or where the lava flow resurface process is faster than the tectonic displacement rate. The many soil CO2 surveys carried out so far on Mt. Etna for the recognition of anomalous soil degassing associated with visible or buried faults over large areas were aimed at measuring the concentration (both static or “dynamic”, the latter according to the method proposed by Gurrieri and Valenza, 1988) of CO2 in the shallow soil (at depth of about 50 cm) along profiles of sampling points with a sampling step ranging from 20 m to 250 m (D'Alessandro et al., 1992; Giammanco et al., 1997; Azzaro et al., 1998; Giammanco et al., 1998a, 1999), or over irregular grids of points with an even larger (about 700 m) sampling step (Giammanco et al., 1993, 1995; Bonfanti and

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Giammanco, 2009). However, none of the previous works involved high spatial frequencies of sampling points within single areas, and they did not consider measuring the gas output from different sources of CO2 (i.e., magmatic/hydrothermal, biogenic or mixed) in the studied areas. The first aim of the present work was, therefore, to perform a large number of soil CO2 efflux measurements (in the order of thousands) over equally-spaced grids of sampling points in two structurally active areas of the volcano that are characterized by rather different tectonic styles and stratigraphic sequences of rocks (sedimentary and/or volcanic rocks) in the shallowest crustal layers. In this way the sampling points covered wide surface areas (several square kilometres) with a sampling step adequate both to the size of the structures to be detected and to the surface extension of the surveyed areas. The second goal was to perform an accurate statistical analysis of the acquired data in order to recognize different populations of soil CO2 effluxes. For the first time in a soil gas study on Mt. Etna, the significant number of measurements

carried out allowed a more reliable statistical analysis of the data acquired, which helped define the most appropriate statistical tools to be used. The third goal was to study how those populations relate to different sources of gas and to the changes in the level of volcanic activity at Mt. Etna during the time span of the present investigation. The final goal was to estimate the CO2 output from each area investigated, discriminating between biogenic and magmatic/hydrothermal contributions of this gas to its total output. 2. Study area Two areas of Mt. Etna were chosen for the purpose of this work (Fig. 1): an area of about 2.5 km2 just to the West of the village of Santa Maria di Licodia on the lower south-western flank of the volcano, and an area of 14 km2 located on Etna's western flank at an altitude between 1100 and 1900 m a.s.l., along the active W-Rift. The

Fig. 1. Simplified volcano-tectonic map of Mt. Etna volcano (modified from Acocella & Neri, 2003) with the location of the two areas (inset boxes) where soil CO2 efflux measurements have been carried out. Box A: Santa Maria di Licodia (SML) area; box B: W-Rift (WR) area. P39 is the reference site where periodic CO2 efflux measurements were carried out during 2005–2008 (see text). Also shown are the three rift zones of the volcano (NE Rift, W Rift, S Rift). TFS: Timpe Fault System; VB: Valle del Bove morphological depression; Grey areas indicate the outcrop of sedimentary rocks. Altitudes are in metres above sea level.

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two areas are important within the structural framework of Mt. Etna because they represent case studies related to two different geological and structural settings. Indeed, the Santa Maria di Licodia village area is characterised by sedimentary rocks (mainly clay) covered by a thin lava flows succession. According to geological studies (Kieffer, 1973; Romano, 1982; Groppelli et al., 2008; Groppelli and Norini, submitted for publication) the area was mainly active at the end of the Ellittico volcano activity (Coltelli et al., 1994), with the opening of numerous eruptive fissures (called “Biancavilla domes”), NE–SW-oriented. This area is also close to major active faults, such as the Ragalna Faults and their associated structures, produced by flank instability of the southern sector of the volcano (Neri et al., 2007). The W-Rift is one of the three main volcanic rift zones that affect the Mount Etna edifice. Although this area shows evidence of very recent activity (the last eruption occurred in 1974), only recently was it investigated, both during the compiling of the new geological map of Mt. Etna (Bellotti et al. 2007, 2010; Groppelli et al., 2008) and during recent geophysical studies (Mattia et al., 2007). The W-Rift is made of more than 40 eruptive fissures, with a roughly ENE–WSW to E–W orientation (Bellotti, 2008). The eruptive fissures date back to the last 15 ka (Mongibello volcano) (Branca et al., 2004, 2008) and are often associated to lava flow fields that show a high resurface process and cover morphological scarps and volcanics related to the previous activity. During the period 2005–2008, the eruptive activity of Mt. Etna was intense. The first months of 2005 were marked by the final development of an almost purely effusive flank eruption that occurred between early September 2004 and early March 2005 (Neri and Acocella, 2006). Afterward, during the winter 2005–2006 minor explosive activity occurred in the summit area, with an increase in late June–early July 2006 (Neri et al., 2006). A new eruptive episode, with lava effusion, started at one of the four summit craters of Mt. Etna (namely, the South– East Crater, hereinafter indicated as SEC) on July 14th, 2006. Lava was emitted from a series of vents located on the SE slopes of the SEC cone, until July 24th, when all activity ceased (Neri et al., 2006; Behncke et al., 2008). Eruptive activity resumed in late summer 2006, lasting from August 31st until December 14th (Calvari and Behncke 2006, 2007; Calvari et al. 2006; Behncke et al., 2008). It consisted of 20 eruptive episodes at or near the summit of the SEC cone, accompanied, from October 12th onward, by periodic effusive activity from several vents to the E-SE and W-SW of the SEC. A new sequence of explosive eruptions started from the SEC at the end of March 2007, producing four short episodes of lava fountains in about 1.5 months (Patanè et al., 2008). A more continuous mild explosive activity started at the SEC in mid-August 2007, but it soon increased in intensity until culminating in a powerful and long-lasting (about 12 h) lava fountaining episode on 4–5 September 2007 (Andronico et al., 2008; Patanè et al., 2008). This was followed on 23–24 November by another vigorous lava fountaining episode that lasted about 6 h (Patanè et al., 2008). Starting from January 2008, mild explosive activity resumed at the summit craters. On May 10th, intense strombolian activity occurred at the SEC followed by a short lava fountain and lava effusion episode (Cannata et al., 2009a, b). On May 13th, following an intense seismic swarm a new flank eruption started on the upper Eastern slopes of the volcano with an initial strong explosive activity along a new eruptive fissure (Aloisi et al., 2009; Cannata et al., 2009a, b). In the following months, eruptive activity at the new fissure was characterized by intermittent explosive activity and constant lava effusion at an average low rate, until the end of the eruption on July 7th, 2009 (Giammanco, 2009). 3. Methodology: soil CO2 data collection and analysis The area of Santa Maria di Licodia (hereinafter indicated as SML) was surveyed in November 2005 and May 2006, and 524 soil CO2

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efflux measurements were carried out in total (Fig. 2). The area of the W-Rift (hereinafter indicated as WR) was surveyed in September 2007 and June 2008, with a total of 1616 measurement points (Fig. 2). Soil CO2 effluxes were measured using the accumulation chamber method, which consists of measuring the rate of increase of the CO2 concentration inside a cylindrical chamber opened at its bottom placed on the ground surface. The chamber has an internal fan to achieve an efficient gas mixing and is connected with a portable NDIR (nondispersive infrared) spectrophotometer (PP Systems, UK, mod. EGM4). The change in concentration during the initial measurement is proportional to the efflux of CO2 (Tonani and Miele, 1991; Chiodini et al., 1998). This is an absolute method that does not require corrections linked to the physical characteristics of the soil. The method was tested in the laboratory with a series of replicate measurements of known CO2 effluxes (Giammanco et al., 2007). The average error was about ±5%, which is assumed as a random error in the natural emission rates. The mean difference between measured effluxes and imposed ones was + 1.7%, thus significantly different from the value (about −12.5%) found by Evans et al. (2001), probably due to the different and evidently more efficient type of measurement system used in this work. The sampling strategy consisted of measuring soil CO2 effluxes on grids of points kept as regular as possible, compatibly with the logistics (i.e., no measurements on inaccessible grounds such as recent lava flows, overgrown bushes, very steep slopes or paved surfaces). The sampling step ranged between 50 and 100 m. In an active volcanic area such as Mt. Etna soil CO2 degassing is generally influenced by changes both in environmental parameters and in the activity level of the volcano (e.g., Hinkle, 1990; Aiuppa et al., 2004). On Mt. Etna, air temperature is the environmental parameter that most affects soil CO2 efflux measurements when these are performed during spatial surveys or when repeated in the same site but with a long (N1 day) sampling period (Giammanco et al., 1995; Bruno et al., 2001; Giammanco and Bonfanti, 2009). Barometric pressure seems to affect soil CO2 efflux measurements, together with air temperature, only when these are repeatedly performed in the same site with a short (b1 day) sampling period (Cannata et al., 2009a, b). A similar behaviour has been observed in other volcanic systems (Rogie et al., 2001; Granieri et al., 2003; Pérez et al., 2006). Environmental parameters have a typical cyclic variation that induces increases in soil CO2 emissions during the summer season due to a combined effect of increased biogenic activity in the soil during the warm period, increased soil permeability due to enhanced evaporation of pore water and possibly also to thinning of the water table (Giammanco et al., 1995; Aiuppa et al., 2004; Giammanco and Bonfanti, 2009). Changes in Mt. Etna's activity level have a strong impact on diffuse CO2 emissions, because the CO2 emitted from the volcano's flanks is largely derived from progressive degassing of upward-migrating fresh, gas-rich magma (Giammanco et al., 1995; Bruno et al., 2001; Badalamenti et al., 2004; Giammanco and Bonfanti, 2009). Therefore, observation of remarkable temporal changes in the emission of “magmatic” CO2 from an anomalous area on Mt. Etna can be useful in detecting changes in the pressure conditions of magma at depth, possibly leading to eruptions (Giammanco et al., 1995; Bruno et al., 2001; Badalamenti et al., 2004; Giammanco and Bonfanti, 2009). Measurements were carried out in two distinct periods of the year, several months apart (six months in the case of the SML area, eight months in the case of the WR area) in both areas surveyed during the investigation. However, this could make comparing the data sets from both couples of surveys difficult, because of the above mentioned influences on the absolute values of diffuse degassing. In order to test if the sets of values from the two surveys in each area (2005 and 2006 for the SML area, 2007 and 2008 for the WR area) were comparable, we used the t-test analysis (Snedecor and Cochran, 1989), which is one of the most suitable tests to compare data from two independent

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Fig. 2. Data collection areas and spatial distribution of the CO2 samples in each area. a) Location of WR and SML areas on Mt. Etna; b) distribution of soil CO2 efflux sampling points in the WR area during the 2007 and 2008 surveys; c) distribution of soil CO2 efflux sampling points in the SML area during the 2005 and 2006 surveys. The legend refers to boxes b) and c).

sets of samples in the case of normal distributions of values. By means of this test, we checked two contrasting hypotheses: the null hypothesis states that the averages for the CO2 efflux values of the two surveys considered are not statistically different, therefore the two sets of data are a single homogeneous population. Conversely, if the averages of two sets are statistically different, the null hypothesis must be rejected and then the two sets of data can be considered as independent groups. We also tested if the distribution of data in each single survey was normal or not by using some basic statistical tools such as frequency distribution analysis and calculating the geometric mean, median and skewness of sample populations. This is a necessary step because soil CO2 values, following one of the fundamental laws of geochemistry (Ahrens, 1954), are usually log-normally distributed (e.g. Lewicki et al., 2005, and references therein) and hence raw data need be transformed into their log values. Furthermore, in order to compare the statistical distribution of samples in the different data sets collected, we transformed our data by using the so-called standard normal form (Davis, 1986), obtained by subtracting the mean of a single population from each value of that population and then by dividing the results by the standard deviation of the same population. This operation “filters out” the different environmental conditions under which the data were collected, thus allowing a statistical comparison of their frequency distribution independently of the absolute values of soil CO2 efflux.

In each data set (November 2005 and May 2006 surveys for the SML area, September 2007 and June 2008 surveys for the WR area) we assessed both the type of data distribution (unimodal or polymodal) and the anomaly thresholds of soil CO2 effluxes by using log probability plots, assuming a lognormal distribution of efflux values from each survey. Sinclair (1974) discussed that in such plots changes in the slope of plotted values are indicative of separate populations of data. Following the graphical procedure of Sinclair (1974), the GraphicStatistical Approach (GSA) described by Chiodini et al. (1998) was applied in order to partition complex distributions of data into different normal (or log-normal) populations and to estimate the proportion (fi), the mean (Mi), and the standard deviation (σi) of population i. This method has been successfully applied to the results of CO2 efflux surveys not only to separate background populations from anomalous CO2 efflux populations, but also to compute the total CO2 output, and relative uncertainties, from the different CO2 sources active in the surveyed areas. Because the populations of efflux values individuated with the log probability plots have log-normal distributions, the mean values of CO2 efflux (φCO2) that were used for the CO2 output calculations and the central 95% confidence interval of the mean were estimated with Sichel's t-estimator (David, 1977). Lastly, we used Natural Neighbor as interpolation method to produce two-dimensional distribution maps of soil CO2 efflux in the studied areas and display the surface areas affected either by magmatic or biogenic CO2 degassing.

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4. Results 4.1. SML area The results obtained from the two surveys in this area are summarised in Fig. 3a. Values for the 2005 survey ranged from 1.7 to 165.8 g m−2 d−1, with arithmetic mean of 26.2 g m−2 d−1 and standard deviation of about 18.5 g m−2 d−1, whereas values for the 2006 survey ranged from 0.2 to 135.8 g m−2 d−1, with arithmetic mean of 14.1 g m−2 d−1 and standard deviation of about 15.3 g m−2 d−1. The distribution of values in both surveys shows a divergence between geometric mean and median values of each data set and high positive values of the skewness obtained from their frequency distributions (Fig. 3). This is indicative of an asymmetry with respect to a classical Gaussian curve distribution due to a surplus in relatively high CO2 values, thus suggesting a log-normal distribution of the data. Therefore, we calculated the Log10 of all efflux values measured and we operated all further statistics on these transformed data, which resulted in clear Gaussian distributions as indicated by the results of the Kolmogorov–Smirnov normality test (Table 1). The results of the t-test analysis (Snedecor and Cochran, 1989) on the Log10 CO2 efflux values of the 2005 and 2006 surveys of the SML area (Table 2) show that the null hypothesis can be rejected at the 5%

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(α = 0.05) level of significance (i.e., the calculated probabilities for each set are much lower than the critical limit in the case of a valid null hypothesis), therefore the couples of data sets are statistically different and must be considered independent. The logarithmic probability plot of the Log10 values of efflux data of the 2005 survey (Fig. 4a) indicates four inflection points, respectively at the percentiles 34, 59, 84 and 95, suggesting the presence of five distinct populations. The average soil CO2 efflux values of the five populations are 11.0 g m−2 d−1, 21.5 g m−2 d−1, 31.3 g m−2 d−1, 46.9 g m−2 d−1 and 84.2 g m−2 d−1, respectively. The logarithmic probability plot for the efflux data of the 2006 survey (Fig. 4b) shows four inflection points, too, respectively at the percentiles 59, 90, 94 and 98. As for the 2005 survey, therefore, the presence of five distinct population is suggested. The average soil CO2 efflux values of these five populations were 7.1 g m−2 d−1, 17.2 g m−2 d−1, 32.1 g m−2 d−1, 44.8 g m−2 d−1 and 92.1 g m−2 d−1, respectively. 4.2. WR area The results obtained from the two surveys in this area are summarized in Fig. 3b. Values for the 2007 survey ranged from 0.2 to 112.3 g m−2 d−1, with arithmetic mean of 16.7 g m−2 d−1 and standard

Fig. 3. Percentage frequency histograms of CO2 efflux values from a) SML and b) WR areas. Inset tables show the main statistical parameters of each distribution. See text for further details.

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Table 1 Basic statistics for the Log10 soil CO2 efflux data from the SML area. All results of the Kolmogorov–Smirnov test are calculated at the 5% significance level and indicate that in both the surveys the data were significantly drawn from a normally distributed population (i.e., the probability of normality is higher than the critical values calculated for the given degrees of freedom). DF = degrees of freedom; CV = critical values. Descriptive statistics on Log10-transformed CO2 efflux data N total Mean Stand. Dev. Skewness Minimum Median Maximum 2005 282 survey 2006 242 survey

1.33

0.29

−0.38

0.23

1.36

2.22

0.98

0.39

−0.57

−0.62

0.99

2.13

Results of the Kolmogorov–Smirnov normality test for the Log10-transformed data

2005 survey 2006 survey

DF

CV

Prob N D

282 242

0.063 0.074

0.204 0.134

deviation of 15.1 g m−2 d−1, whereas values for the 2008 survey ranged from 0.2 to 311.5 g m−2 d−1, with arithmetic mean of 27.5 g m−2 d−1 and standard deviation of about 30.1 g m−2 d−1. As for the SML area, the frequency distribution of efflux values from the WR area suggests their log-normal distribution, as indicated by the basic statistics of Fig. 3b (divergence between the geometric mean and median values and positive skewness due to a surplus in relatively high CO2 values). Therefore, also in this case all further statistical analyses were performed on the Log10 transformed data, whose distributions are Gaussian, according to the results of the Kolmogorov–Smirnov normality test (Table 2). As for the SML area, the comparison between the 2007 and 2008 data sets for the WR area was tested using the t-test analysis (Snedecor and Cochran, 1989). The results (Table 3) show that for the two surveys of the WR area the null hypothesis (i.e., the two distributions are not significantly different) must be rejected at the 5% (α=0.05) level of significance, therefore the couples of data sets are statistically different and, also in this case, considered independent. The probability plot of the Log10 values of efflux data of the 2007 survey (Fig. 4c) indicates four inflection points, respectively at the percentiles 78, 89, 95 and 98, suggesting the presence of five distinct populations. The average soil CO2 efflux values of the five populations were 11.3 g m−2 d−1, 28.3 g m−2 d−1, 37.3 g m−2 d−1, 55.4 g m−2 d−1 and 80.4 g m−2 d−1, respectively. The logarithmic probability plot for the efflux data of the 2008 survey (Fig. 4d) shows four inflection points, too, respectively at the percentiles 74, 90, 98 and 99. As for the 2007 survey, therefore, the presence of five distinct population is suggested, whose average soil CO2 efflux values were, respectively, 15.4 g m −2 d −1 , 46.6 g m −2 d −1 , 85.6 g m −2 d −1 , 142.4 g m−2 d−1 and 275.2 g m−2 d−1. Table 2 Basic statistics for the Log10 soil CO2 efflux data from the WR area. All results of the Kolmogorov–Smirnov test are calculated at the 5% significance level and indicate that in both the surveys the data were significantly drawn from a normally distributed population (i.e., the probability of normality is higher than the critical values calculated for the given degrees of freedom). DF = degrees of freedom; CV = critical values.

4.3. Standardized data analysis The results of the t-test analysis (Snedecor and Cochran, 1989) described above entail the use of data standardization. The procedure for the standard normal form described in Davis (1986) provides a useful method to compare both the different frequency distributions of data from each survey and their statistical properties. Standardized values from the two surveyed areas are summarized in Fig. 5. Standardized data from the SML area range from −1.32 to 7.55 for the 2005 survey and from −0.91 to 7.98 for the 2006 survey (Fig. 5a), whereas data from the WR area range between −1.09 and 6.33 for the 2007 survey and between −0.90 and 9.43 for the 2008 survey (Fig. 5b). As discussed above, comparison between couples of datasets collected during November 2005 and May 2006 at SML and September 2007 and June 2008 at WR reveals their statistical difference, shown by the parameters used in the standardized descriptive statistics. In particular, the histograms of frequency distribution of November 2005 and May 2006 data at SML and June 2008 data at WR are characterized by multiple modes. On the contrary, the September 2007 data show a unimodal shape in correspondence of the −0.71 value, thus confirming what was shown by the analysis on the frequency distribution of “raw” CO2 efflux values (Fig. 3b). The WR June 2008 data show a large difference between their minimum and maximum values, with a range of 10.34. This reflects a wide statistical distribution of data in agreement with the wide spread between lower and higher “raw” CO2 values measured in this survey. Conversely, the WR September 2007 data, with a range of only 7.42, highlight a low spread between their maximum and minimum values, reflecting the small difference between low and high “raw” values (Fig. 5b). Finally, the November 2005 and May 2006 datasets from the SML area show an intermediate behaviour, with almost similar ranges (8.87 and 8.89, respectively) (Fig. 5a). A further analysis points out the presence of a negative asymmetry for each dataset, as demonstrated by the positive skewness values (2.59 and 3.89, respectively, for SML November 2005 and May 2006; 1.99 and 3.21, respectively, for WR September 2007 and June 2008). The positive skewness values reflect higher frequencies of lower values in all of our sample distributions, but also confirm the presence of many CO2 efflux values that are significantly higher, and hence “anomalous”, than those expected in a normal Gaussian distribution (Fig. 5). The kurtosis analysis reveals a leptokurtic distribution of each dataset, with values of 12.75 and 21.62, respectively, for SML November 2005 and May 2006 surveys and 5.64 and 18.24, respectively, for WR September 2007 and June 2008 surveys. The peak of each statistical distribution curve appears sharpened with a marked clustering of values close to their maximum (Fig. 5). As a final remark, the above analysis shows the overall good match between the statistical properties of our raw data and that of their standard normal form, thus allowing us to proceed in the statistical analysis of raw values of soil CO2 efflux (and their Log10 transform) without any significant loss of statistical meaning. 5. Recognition and spatial distribution of anomalous soil CO2 emissions

Descriptive statistics on Log10-transformed CO2 efflux data N total Mean Stand. Dev. Skewness Minimum Median Maximum 2007 733 survey 2008 883 survey

1.05

0.43

−0.66

−0.62

1.09

2.05

1.22

0.47

−0.49

−0.62

1.26

2.49

Results of the Kolmogorov–Smirnov normality test for the Log10-transformed data

2007 survey 2008 survey

DF

CV

Prob N D

733 883

0.050 0.040

0.051 0.116

5.1. Discrimination of different CO2 sources Table 4 shows the estimated parameters of the partitioned populations recognized in each survey, as well as the estimate of the diffuse CO2 output obtained using the GSA method (Chiodini et al., 1998). The wide range of CO2 effluxes measured in the different surveys/ areas indicates a large variability in the release of CO2 through diffuse emissions. This is in agreement with previous studies (Toutain and Baubron, 1999; Mörner and Etiope, 2002 and reference therein) that highlighted how CO2 effluxes can be extremely variable in geothermal

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Fig. 4. Log probability plots of CO2 efflux data collected during a) SML-2005, b) SML-2006, c) WR-2007 and d) WR-2008 surveys. Changes in the slope of plotted data indicate different populations of efflux values. Circles highlight the flexure points. Inset graphs in each plot represent the partitioned populations recognized in each survey.

and/or tectonically-active areas. This behaviour derives from the mechanism that rules gas transport through the ground: faults are the main pathway for the escape of magmatic gas towards the surface as they are highly permeable zones within the crust. Comparison of the soil CO2 efflux values from each distinct population identified with the probability plots of Fig. 4 allows to quantify the contribution of different sources of CO2 to the total CO2 degassing in the studied areas. In addition, it indicates the temporal variation of degassing rate of each CO2 source during the different sampling surveys carried out in the two studied areas. Table 3 Results of the t-test performed on Log10 soil CO2 efflux data for the couples of surveys carried out in each studied area. The small P-value, which is less than the 0.05 significance level, indicates that in both cases the null hypothesis can be rejected. The test shows similar results both if equal variances of the two samples are assumed and if they are not. DF = degrees of freedom. SML area, 2005–2006

t statistic

Equal variance assumed Equal variance not assumed

−11.36 −11.11

WR area, 2007–2008

t statistic

Equal variance assumed Equal variance not assumed

−7.62 −7.69

DF 522.00 439.88

Prob N |t| 7.19E−27 1.87E−25

DF

Prob N |t|

1614.00 1599.98

4.25E−14 2.61E−14

In all surveys, populations with CO2 efflux values b36 g m−2 d−1 can be considered as background degassing and hence representative of soil respiration, in agreement with the results obtained in different areas characterized by various types of soil and vegetation. In particular, CO2 effluxes associated with areas characterized by scarce vegetation, uncultivated meadows, mediterranean maquis or semiarid steppe have average values of 7.6 g m−2 d−1 (Raich and Schlesinger, 1992; La Scala et al., 2000; Mielnick and Dugas 2000; Raich and Tufekcioglu, 2000; Angell et al., 2001; Frank et al., 2002; Maestre and Cortina, 2003; Obrist et al., 2003; Reth et al., 2005). Conversely, in the studied areas measured efflux values above 62 g m−2 d−1 (i.e., the lowest value belonging to populations E) can be considered as anomalous and related mainly to degassing from the hydrothermal/magmatic system. Efflux values comprised between 36 and 62 g m−2 d−1 (belonging to populations B, C and D), would reflect mixing of both sources, with increasing contribution of the hydrothermal/magmatic source on moving towards higher CO2 values. 5.2. Spatial and temporal behaviour of different CO2 populations and estimate of their output Comparison between the average efflux values of the different populations identified with the log-probability plots in each survey highlighted several points (Table 4, Fig. 6). First, in all datasets

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Fig. 5. Percentage frequency histograms of standardized CO2 efflux values (dimensionless units) from a) SML and b) WR areas. The inset table shows the main statistical parameters of each standardized distribution. See text for further details.

population A showed mean CO2 efflux values comprised between 7.1 g m−2 d−1 (SML-May 2006 survey) and 15.5 g m−2 d−1 (WR-June 2008 survey), comparable with one another and always lower than population B values. The average percent variation between average CO2 values of populations A and B in all datasets is close to 60%, with the lowest difference (41%) being observed for SML-November 2005 survey and the highest difference (70%) for WR-June 2008 survey. Second, all populations of the SML-May 2006 survey showed the lowest mean efflux values compared with the respective populations of the other surveys/areas, except for its population E, which was slightly higher than SML-November 2005 survey and WR-September 2007 surveys. Third, all populations of WR-June 2008 survey showed the highest mean CO2 values. Fourth, the difference between the average values of each population of WR-June 2008 survey and those of the respective populations of the other surveys is increasingly higher on moving from populations A to E. If the high efflux values in all populations of WR-2008 survey were only due to enhanced biogenic activity in the soil, then we would observe the opposite effect, with highest positive difference in population A and lowest in population E. Therefore, a reasonable explanation to the behaviour observed in the populations of

WR-June 2008 survey is to assume both a meteorological effect (e.g., gas-pumping effect due to strong changes in barometric pressure and/or air temperature, as described in Hinkle, 1990 and in Giammanco et al., 1995) and a stronger increase in the emission of magmatic CO2 through the soil. The former produces an increasing response in the high-efflux populations relevant to soils with higher permeability likely to occur near faults, the latter has a growing effect on populations from B to E, that is with increasing contribution of magmatic CO2. In all surveys, the CO2 output (in t d−1) from each population was calculated multiplying the average CO2 efflux of the –th population by the corresponding surface (Si, where subscript “i” stands for each population from A to E recognized by means of statistical estimations). An evaluation of Si is obtained by multiplying the surface of each study area by the corresponding proportion fi of the population (i.e., Si = fiS). The total CO2 output from each studied area results from the summation of each population contribution (Table 4). Similarly, the central 95% confidence interval of the mean is used to calculate the uncertainty of the total CO2 output estimation of each population. Besides, we calculated the total CO2 output for each survey both considering all populations and without considering populations A

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Table 4 Estimated parameters of partitioned populations at each area where soil CO2 effluxes were measured. Central 95% confidence limits for average CO2 effluxes and output values are shown in brackets. Subscript i refers to the ith partitioned population. CO2 outputs are calculated multiplying the average CO2 efflux of the ith partitioned population by the corresponding surface (see text). Total CO2 output for each survey is the summation of all single outputs pertaining to each population (in square brackets is the total “magmatic” output calculated without considering population A). Survey

Population

No. of points

fi (%)

SML-2005

A B C D E Total A B C D E Total A B C D E Total A B C D E Total

97 68 72 32 13 282 143 75 10 9 5 242 568 82 44 28 11 733 652 147 73 8 3 883

34.4 24.1 25.5 11.3 4.6 100 59.1 31.0 4.1 3.7 2.1 100 77.5 11.2 6.0 3.8 1.5 100 73.8 16.6 8.3 0.9 0.3 100

SML-2006

WR-2007

WR-2008

φ CO2 (g m−2 d−1)

min–max φ CO2 (g m−2 d−1)

11.1 21.5 31.3 46.9 84.2

(10.3–12.0) (21.2–21.9) (30.8–32.0) (45.1–49.5) (75.6–99.3)

1.7–16.6 17.0–25.2 25.9–38.4 39.1–60.7 62.9–165.8

7.1 17.2 32.1 44.8 92.1

(6.3–8.1) (16.5–18.1) (31.5–33.3) (41.4–51.2) (77.0–133.3)

0.2–11.3 11.5–27.1 28.1–37.9 39.8–64.1 77.3–135.8

11.3 28.3 37.3 55.4 80.4

(10.3–12.7) (28.1–28.5) (36.8–38.0) (53.7–57.8) (75.1–89.4)

0.2–25.2 25.2–33.1 33.1–45.1 46.8–68.6 68.6–112.3

15.4 46.6 85.6 142.4 275.2

values, in order to highlight the contribution of the magmatic/hydrothermal CO2 to the total output of this gas (Table 4). It must be noted that the gas output values strongly depend on the surface pertaining to the same population. This explains why the magmatic CO2 output (i.e., that calculated without population A effluxes) in the SML-2006 survey is so low compared to the other surveys: the surface values pertaining to populations B through E were remarkably lower than those of the same populations in the other surveys, despite their similar average efflux values, except for survey WR-2008. This can be due either to the absence of tectonic structures that could produce wide anomalies in the diffuse CO2 emissions or, to a lesser degree, to the extensive urbanization of the area surveyed in 2006. Conversely, the WR-2008 survey shows the highest output of magmatic CO2, although the surfaces covered by the points pertaining to the partitioned populations of data are comparable with those of the other surveys. This is clearly the result of the exceptionally high values of magmatic CO2 degassing measured during that survey (Fig. 6).

Figure 6. –Temporal behaviour of average CO2 efflux values for partitioned populations in each survey carried out on Mt. Etna.

(14.1–17.2) (45.5–47.8) (82.2–89.9) (140.4–146.6) (246.4–311.5)

0.2–36.0 36.2–63.8 64.1–133.2 133.4–159.6 233.0–311.5

Si (m2)

CO2 output (t d−1)

514,617 360,762 381,983 169,770 68,969 1,496,102 646,544 339,097 45,213 40,692 22,606 1,094,152 4,365,278 630,199 338,155 215,190 84,539 5,633,360 6,075,546 1,369,793 680,238 74,547 27,955 8,228,078

5.7 (5.3–6.2) 7.8 (7.6–7.9) 12.0 (11.8–12.2) 8.0 (7.6–8.4) 5.8 (5.2–6.9) 39.2 [33.5] 4.6 (4.1–5.2) 5.8 (5.6–6.1) 1.5 (1.4–1.5) 1.8 (1.7–2.1) 2.1 (1.7–3.0) 15.8 [11.2] 49.3 (44.8–55.3) 17.8 (17.7–18.0) 12.6 (12.4–12.8) 11.9 (11.6–12.4) 6.8 (6.3–7.6) 98.4 [49.1] 93.7 (85.6–104.5) 63.8 (62.3–65.5) 58.2 (55.9–61.1) 10.7 (10.5–11.0) 7.7 234.1 [140.4]

5.3. Magmatic CO2 emissions from SML and WR areas within the temporal pattern of diffuse magmatic degassing at Mt. Etna In order to analyse the variations in the magmatic component of diffuse CO2 emissions observed in the different surveys carried out throughout the period 2005–2008, the sum of all “magmatic” soil CO2 effluxes (i.e. calculated without considering the values belonging to “background” populations A) for each survey was compared with the soil CO2 efflux values measured in a “reference” site. This site (Fig. 1) is named P39 and it is a single point located on the lower south-western flank of Mt. Etna, about 2 km southwest of the town of Paternò (altitude of about 115 m a.s.l) and approximately 20 km SSW of Etna's summit craters. It is a mofette characterised by huge emissions of CO2 from the ground and by significant emissions of CH4, He and other reduced gases such as H2 and CO, with negligible air contamination (Giammanco et al. 1998a; Pecoraino and Giammanco, 2005). Both the chemical and the isotopic features of CO2 and He gases emitted at site P39, clearly point to their magmatic origin (D'Alessandro and Parello, 1995; Giammanco et al., 1998a; Bruno et al., 2001; Pecoraino and Giammanco, 2005) and more specifically from degassing of an 8– 12 km-deep, mantle-like source of magma (Giammanco et al., 1998a; Italiano et al., 1999; Caracausi et al., 2003). These gases reach the surface through a NE–SW-directed regional fault (Caracausi et al., 2003), that would correspond to one of the main directions of magma rise beneath Etna (Rasà et al., 1995). During the period 2005–2008, soil CO2 efflux was periodically measured at site P39 with the accumulation chamber method by reoccupation of the sampling point. The sampling frequency varied from almost bi-monthly in 2005 and 2006 to almost monthly during 2007 and 2008 (Fig. 7). In general, the temporal variations of magmatic CO2 efflux observed at this site indicate an apparent seasonal oscillation, with maximum values during summer (particularly between June and August) and minimum values during winter. This behaviour is confirmed by a spectral analysis performed on the efflux data from this site that indicate a clear cyclic component with period close to one year (Fig. 8), in agreement with the results of previous studies on the temporal changes of soil CO2 emissions at Mt. Etna (Giammanco et al.,

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Fig. 7. Temporal pattern of soil CO2 efflux values measured at sites P39 (located on the lower SW flank of Mt. Etna) during the period January 2005–December 2008, compared with the sum of all “magmatic” efflux values (i.e. without considering background populations A) for each survey from SML and WR areas. White boxes on top of the plot indicate periods of continued eruptive activity of Mt. Etna (see text for details).

1995; Caltabiano et al., 2004; Giammanco and Bonfanti, 2009). Such a cyclic variation is due to the effect of meteorological parameters and in particular of air temperature (gas-pumping effect, Hinkle, 1990; Giammanco et al., 1995). Correlation between soil CO2 effluxes from site P39 and the corresponding mean air temperature values was actually found to be significant (R = 0.60). Another interesting feature of CO2 efflux values at site P39 is the remarkable decrease in degassing observed in 2007 and mostly in 2008. According to recent geochemical models developed to describe the mechanisms of natural degassing in the area of Paternò (Giammanco et al. 1995; Bruno et al. 2001; Caracausi et al. 2003; Aiuppa et al. 2004; Caltabiano et al. 2004; Pecoraino and Giammanco 2005), strong decreases in the usually high soil CO2 emissions in this side of Etna, and particularly at site P39, are related to massive magma migration from the deep magma reservoir towards upper levels in the feeder system of Mt. Etna. By contrast, such episodes of magma up-rise produce increases in diffuse gas emissions in other parts of the volcano that are closer to the summit area and mostly where magmatic soil degassing occurs along relatively shallow faults connected with the uppermost portions of Etna's plumbing system. These sequences of spatial/temporal migration of the anomalies in diffuse CO2 degassing at Mt. Etna usually precede the onset of flank eruptions by months to weeks (Giammanco et al., 1995; Bruno et al. 2001; Caracausi et al. 2003; Aiuppa et al. 2004; Caltabiano et al. 2004; Pecoraino and Giammanco 2005). In the case of the above described decrease in degassing at site P39, that anomaly could be related to

upward magma migration preceding the long-standing 2008–2009 flank eruption. By plotting the average “anomalous” effluxes (i.e., average of all efflux values higher than the upper limit of populations A) for each of the four surveys in the SML and WR areas together with the values from site P39 (Fig. 7), it is evident that: i) anomalous CO2 effluxes from the investigated areas were on average comparable to the lowest values from site P39, where CO2 degassing is entirely of magmatic origin, thus further supporting the magmatic contribution to this type of degassing; ii) the average anomalous CO2 efflux values from the SML and WR areas were stable and relatively low during the SML-2005, SML-2006 and WR2007 surveys, whereas they were significantly higher during the WR2008 survey, as already discussed in previous paragraphs. Although this could be a result of seasonal effects, as the 2008 survey was the only one carried out in the summer time and hence when meteorological effects are generally the highest (Hinkle, 1990, Giammanco et al., 1995), we cannot rule out the possibility that the comparatively high anomalous value of WR-2008 survey was at least in part caused by higher magmatic degassing. This hypothesis is mainly based on the peculiar temporal correlation between the strong anomalous degassing recorded during the WR-2008 survey and occurrence of the 2008–2009 flank eruption. 5.4. Natural Neighbor spatial analysis In order to represent the spatial distribution of CO2 efflux values, we interpolated the collected dataset of point values from SML and WR areas. The advantage of this analysis consists in the possibility of superimposing CO2 efflux maps on the volcano topography and verifying the two-dimensional distribution of magmatic and biogenic CO2 emissions. The interpolation method chosen is the Natural Neighbor algorithm. It finds the closest subset of input samples to a query point and applies weights to them based on the increasing distance from the query point in order to interpolate new values where no sampling point has been measured (Sibson, 1981). As described above, the four data sets are different both in extension and in step range, the latter being 75 m for SML areas and 100 m for WR areas with a closer step of 50 m in a small sector of the WR area located just NE of Mt. Minardo (Fig. 2). We used standardized data in order to “filter” the raw values, and hence overcome the statistical differences in the distributions of CO2 effluxes or their differences in rank. Use of standardized data also helped show the spatial distribution of interpolated values of the four surveys (SML-2005, SML-2006, WR2007, and WR-2008) by means of a common scale (Fig. 9). The distribution maps of Fig. 9 show that the main anomalies are located in the westernmost and south-easternmost sectors of the WR area, and in the central and north-easternmost parts of the SML area. In general, CO2 anomalies have a rather scattered distribution, with wide areas characterized by a main contribution of biogenic CO2, such as, for example, the southern sector of SML area and the eastern sector of WR. Anomalous CO2 emission values are often clustered and show a limited surface area extension, usually few hundreds of square metres. A reasonable explanation for this behaviour is that in the investigated areas the structural lineaments that allow the migration of CO2 towards the surface are not so continuous and they probably present a complex geometrical pattern at depth that results from intersection of different tectonic systems (see discussion in Section 6 below). 6. Discussion of results and conclusions

Fig. 8. Periodogram calculated for the 2005–2008 temporal data of soil CO2 efflux from the P39 site. The arrow highlights the periodic component with a 12 month-period. Ordinates in arbitrary units.

The results of the four surveys carried out between 2005 and 2008 to determine soil CO2 effluxes clearly indicate that in each survey, and hence in each of the investigated areas, five distinct populations of CO2 efflux can be recognized, thus indicating a polymodal distribution of this variable. This strongly suggests that diffuse CO2 emissions from Mt. Etna's flanks have various sources and reflect the different

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Fig. 9. Interpolation map of standardized CO2 efflux values (dimensionless units), using Natural Neighbor method. Box a) location of WR and SML areas. The image shows the spatial distribution of biogenic CO2 (green chromatic shade) and volcanic CO2 (orange to red chromatic shade) in b) WR and c) SML areas. Each population is represented by two different tones of the same colour in the legend. Black dashed lines indicate inferred directions of soil CO2 anomalies, likely corresponding to hidden faults.

mechanisms of CO2 migration from its deep magmatic source to the topographic surface. Population A, constituted by the lowest CO2 values, shows similar values in each survey, and a marked difference with respect to the following population B (see paragraph 3.4). As population A represents the biogenic source of CO2, the “gap” between populations A and B marks the limit between purely biogenic and increasingly volcanic CO2. This hypothesis is supported by the analysis of populations A in each survey. In fact, the highest biogenic effluxes were measured in the survey carried out during the summer of 2008, hence when biological activity in the soil is normally the highest. Populations B, C, D and E are likely produced by an increasing contribution of a magmatic source of CO2, even if they have different efflux rates and spatial allocation in each survey session. These differences are primarily produced by the “natural” statistical distributions of the efflux values, which is usual in a wide dataset, but also by the nature of the physical–chemical processes that govern soil degassing in a volcanic area. In fact, the assumption that the CO2 measured at the surface is produced by an active magmatic source and rises up through buried crustal discontinuities implies that the amount of CO2 released to the atmosphere depends on the amount, depth and position of degassing magma inside the feeder conduits of the volcano and it is influenced by the crustal permeability along the structural discontinuities used by gas on its way to the surface. Analyses of the isotopic composition of carbon of CO2 would have helped interpreting volcanic gas contributions and their variations, but this work was mainly focused

on the use of statistics in the definition of anomalous soil degassing, without resorting to expensive and time-consuming laboratory analyses of δ13C(CO2). In any case, recent studies on carbon isotopes from soil CO2 emissions in the active volcanic area of Solfatara di Pozzuoli (Caliro et al., 2008; Chiodini et al., 2008) have shown that the threshold value for isotopically derived biogenic CO2 effluxes is 50 g m−2 d−1 and this value is lower than that obtained from statistical analysis of the same data. This value is slightly higher than the highest one from populations A of this study, but comparable to the values indicated for mixing between biogenic and magmatic sources in the surveyed areas of Mt. Etna, thus indirectly supporting our assumptions. In the surveyed areas, hidden structural discontinuities have been inferred by using the spatial distribution of anomalous soil CO2 degassing carried out through the interpolation maps shown in Fig. 9. Degassing along these faults is not homogeneous in space, as it shows variable intensity according to the fault system under consideration, and it seems not to be constant in time either, because it depends both on the level of volcanic activity and on seasonal influences from meteorological parameters. Despite the rather inhomogeneous distribution of soil gas anomalies, in some cases it was possible to infer prevailing directions in the distribution of anomalous soil degassing (indicated by dashed lines in the maps of Fig. 9). For example, in the SML area a roughly E–Wtrending distribution of CO2 anomalies can be recognized just West of the village of Santa Maria di Licodia (Fig. 9c). In the WR area, soil CO2 anomalies seem to follow a roughly ENE–WSW orientation between

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Mt. Minardo and Mt. Tre Frati in the western sector of the area, which is in agreement with one of the main structural directions of the WRift. Another possible spatial trend can be observed in the southeasternmost part of the WR area (Fig. 9b), near Mt. Albano. It shows a NE–SW orientation, which is close to that of a tectonic lineament recognized by Mattia et al. (2007) in the same area using geophysical prospecting methods. The inferred orientations for possible hidden faults in the studied areas, spanning from E–W to NE–SW, are not unreasonable within the tectonic and volcanic framework of the Etna region, as these orientations match those of one of the main regional tectonic systems that cross Sicily, namely the Comiso–Etna system and its inferred prolongation, the Messina–Capo Vaticano system (Ogniben, 1969; Lo Giudice et al., 1982; Bousquet and Lanzafame, 2004). These structural trends have played a major role in driving the volcanic activity of eastern Sicily since late Miocene (Behncke, 2004; Bousquet and Lanzafame, 2004). They still seem to deeply affect Mt. Etna's volcanic activity, as they are thought to guide the ascent of Etna's magmas from the mantle to the uppermost portions of the crust underneath the volcano (Rasà et al., 1995; Patanè et al., 2008). Furthermore, remarkable release of magmatic fluids in most part of the lower south-western flank of Mt. Etna is suggested by widespread anomalous diffuse magmatic degassing and hydrothermal activity. The former is demonstrated by remarkable CO2 contents dissolved in local ground water (Aiuppa et al., 2004) and high CO2 emissions from soils (Giammanco et al., 1995, 2007), the latter is witnessed by the presence of the “Salinelle” mud-volcanoes (Giammanco et al., 1998a, 2007; Italiano et al., 1999; Caracausi et al., 2003; Aiuppa et al., 2004; Pecoraino and Giammanco, 2005) and a series of travertine deposits in the AdranoPaternò area, both phenomena formed by emergence of thermal waters

produced by interaction between ground water and hot CO2-rich magmatic fluids (Aiuppa et al., 2004; Shaw and Bateman, 2006; D'Alessandro et al., 2007). Such anomalous degassing activity has been going on for several thousands of years, as shown by Romano et al. (1987) who assume that the early stages of activity of Mongibello volcanic cycle (beginning about 15 ka) triggered the circulation of carbonate-rich groundwater along an ENE–WSW-trending fault system. More detailed analysis is under way in order to better assess these issues. Considering the total amount of magmatic CO2 emitted in each survey, it is possible to analyse the behaviour of flank CO2 degassing and infer the role played by volcanic activity between November 2005 and June 2008. The SML-November 2005 survey was carried out in a relative small area with a relatively low number of measurements (282 sampling points). Yet, it revealed a high magmatic CO2 output (33.5 t d−1) compared to the SML-May 2006 survey, which was similar both in terms of surveyed surface and in terms of number of sampling points. This behaviour is evidently the result of the large number of anomalous points measured in the SML-2005 survey, likely due to the interception of at least one hidden fault located just W of Santa Maria di Licodia village and roughly oriented E–W (Fig. 9c). Analysis of the CO2 emission rate from the WR June 2008 survey allows for some considerations on the use of soil CO2 surveys in volcano monitoring. The total volcanic CO2 efflux values in the WR2008 survey was actually 549.8 g m−2 d−1, that is by far the highest registered in all the investigated areas. As above described, the remarkably high degassing rate during this survey could be related partly to meteorological effects and partly to volcanic effects. It must be underlined that this survey was the only one carried out during a major flank eruption, therefore such high diffuse CO2 emissions could

Fig. 10. Schematic model of the inferred CO2 release from ascending magma within Mt. Etna's feeding system and its effect on diffuse CO2 degassing at the surface in the areas surveyed during this study. Red spherical volumes show the zones of temporary magma storage during the periods (A) 2005–2007 and (B) 2008; black arrows indicate inferred directions of the flux vectors of magmatic gas; the larger red arrow indicates the direction of gas-rich magma motion into Mt. Etna's feeding system between 2007 and 2008. The question mark highlights the uncertainty in the location of the magmatic source of CO2 that fed diffuse degassing in the SML area during 2005–2006. Vertical axis not in scale.

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indicate a close connection between the faults belonging to the WR area that affect that sector of Mt. Etna and the magma reservoir that was feeding the 2008–2009 eruption. It should also be noted that, similar to the WR June 2008 survey, the WR September 2007 survey was carried out during a period of summit eruptions (Behncke, 2009). However, this circumstance apparently did not affect the diffuse emissions of magmatic CO2 significantly in that part of Mt. Etna, and the average output value of WR-2007 survey was relatively low and comparable to that of the SML-2005 and SML-2006 surveys. This could suggest different responses of flank degassing according to different styles and types of eruptions ongoing at the time when our surveys were carried out (intermittent mixed summit activity during September 2007, long-standing lava effusion from the volcano's flanks during June 2008) and/or to the different tectonic structures used by magma during its ascent to the surface. A schematic model that shows the spatial and temporal relationships between magmatic degassing at Mt. Etna and tectonic settings in the studied areas is shown in Fig. 10. The figure describes a mechanism of progressive migration of diffuse gas anomalies towards sectors of Etna closer to the central craters as magma migrates towards the surface. The WR area, as a matter of fact, is closer to the main craters of Etna than the SML area and much closer than site P39 (Fig. 1), therefore it was probably more affected by magmatic degassing as magma accumulated closer to the surface during the 2008 flank eruption. In any case, our results raise interesting questions and envisage new contributions for a better understanding of the internal structure of Mt. Etna's feeder system. Finally, our findings further support the importance of temporal monitoring of soil CO2 emissions along selected faults, such as those of the WR area, for volcano surveillance. Acknowledgements We thank the Regional Park of Etna, in particular Dr. Salvatore Caffo, and Aziende Forestali Demaniali–Provincia di Catania, for authorising us to work in protected areas of Mt. Etna. Work funded by the Istituto Nazionale di Geofisica e Vulcanologia–Sezione di Catania, by the CNR– Istituto per la Dinamica dei Processi Ambientali, by the Dipartimento per la Protezione Civile (Italy), project V5/08-Diffuse degassing in Italy (SG), and PRIN 2004 (resp. P. Tartarotti). We also thank Gianluca Norini for his help during the first soil CO2 survey. Soil CO2 surveys were carried out with the help of Marco Azzola, Luca Campagnoli, Elisa Caprino Campana, Tommaso Landi, Laura Locatelli, Chiara Marieni Alberto Masserini, Paolo Mauri, and Emanuele Nugara. We acknowledge two anonymous reviewers for their constructive reviews. References Acocella, V., Neri, M., 2003. What makes flank eruptions? The 2001 Etna eruption and its possible triggering mechanisms, Bull. Volcanology 65, 517–529. doi:10.1007/ s00445-003-0280-3. Ahrens, L.H., 1954. The lognormal distribution of the elements (A fundamental law of geochemistry and its subsidiary). Geochim. Cosmochim. Acta 5, 49–73. Aiuppa, A., Allard, P., D'Alessandro, W., Giammanco, S., Parello, F., Valenza, M., 2004. Magmatic gas leakage at Mount Etna (Sicily, Italy): relationships with the volcanotectonic structures, the hydrological pattern and the eruptive activity. In: Bonaccorso, A., Calvari, S., Coltelli, M., Del Negro, C., Falsaperla, S. (Eds.), Mt. Etna: Volcano Laboratory. American Geophysical Union, Washington, DC, pp. 129–145. doi:10.1029/143GM09. Allard, P., Carbonelle, J., Dajlevic, D., Le bronec, J., Morel, P., Robe, M.C., Maurenas, J.M., Faivre-Pierret, R., Martin, D., Sabroux, J.C., Zettwoog, P., 1991. Eruptive and diffuse emissions of CO2 from Mount Etna. Nature 351, 387–391. Allard, P., Jean-Baptiste, P., D'Alessandro, W., Parello, F., Parisi, B., Flehoc, C., 1997. Mantle-derived helium and carbon in groundwaters and gases of Mount Etna, Italy. Earth Planet. Sci. Lett. 148, 501–516. Aloisi, M., Bonaccorso, A., Cannavo`, F., Gambino, S., Mattia, M., Puglisi, G., Boschi, E., 2009. A new dyke intrusion style for the Mount Etna May 2008 eruption modelled through continuous tilt and GPS data. Terra Nova 1–6. doi:10.1111/j.1365-3121.2009.00889.x. Andronico, D., Cristaldi, A., Scollo, S., 2008. The 4–5 September 2007 lava fountain at South–East Crater of Mt Etna, Italy. J. Volcanol. Geoth. Res. 173, 325–328. doi:10.1016/j.jvolgeores.2008.02.004. Angell, R.F., Svejcar, T., Bates, J., Saliendra, N.Z., Johnson, D.A., 2001. Bowen ratio and closed chamber carbon dioxide flux measurements over sagebrush steppe vegetation. Agric. For. Meteorol. 108 (2), 153–161.

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