A reliable and effective methodology to monitor CO2 flux from soil: The case of Lipari Island (Sicily, Italy). (October 2017)

• Record Type: Journal Article
• Authors: Lelli, M.; Raco, B.
• Is Part Of: Applied geochemistry. Volume 85:Part A(2017)
• Page Start: 73
• Page End: 85
• Publication Date: 2017-10
• Subjects: Accumulation chamber -- CO2 flux from soil -- Time series analyses -- Lipari Island -- Meteorological influence; Environmental geochemistry -- Periodicals; Water chemistry -- Periodicals; Geochemistry -- Social aspects -- Periodicals; Geochemistry -- Periodicals; 551.9
• DOI: 10.1016/j.apgeochem.2017.08.004
• Languages: English
• ISSNs: 0883-2927

Abstract

Abstract: A new unit for continuous monitoring of CO2 flux diffused from soil was developed and preliminary tested in the field at Lipari Island (Sicily, Italy). The developed device (CO2 -S-POT), based on the accumulation chamber method, is an assembled unit easy to install and to manage. Both dimensions (104 × 61 × 75 cm) and weight (32 Kg) of CO2 -S-POT are lower than the equipment which are commonly used for continuous monitoring of diffused CO2 flux. Besides CO2 flux measurements, it is able to measure several environmental parameters [air temperature, soil temperature, atmospheric pressure, Volumetric Water Content (soil VWC%) and soil conductivity]. Laboratory tests showed a very good performance of the assembled system in terms of detection limit (0.1 mol m −2 ·day −1 ), reproducibility (RSD better than 3.5%) and linearity (up to 4 order of magnitude). Field tests (3 months) were performed at Lipari Island (Sicily, Italy), where no continuous monitoring of diffuse CO 2 flux using the accumulation chamber method is ongoing. During field tests, the CO2 -S-POT performed flux measurements every hour, together with monitoring of the environmental parameters. During field tests, no ordinary or extraordinary maintenance was necessary and no missing data were detected. The soil CO2 flux showed strong influence operated by air pressure, air temperature, soil temperature, soil conductivity, Volumetric Water Content and rain. Statistical methods based on Time Series Fourier analysis (TSFA), Principal Component Analysis (PCA) and also Multiple Linear Regression Analysis (MLRA) are used in order to filter the influence of the environmental parameters on the flux data, and, at the same time, to highlight the contribution which could be due from deep system. Raw flux data (in ppm·sec −1 ) are used in time series analysis, since these represent the real physical variable measured in the field, without transformations. In fact, the conversion from ppm·sec −1 to another dimension (i.e. mol·m −2 ·day −1 ) affects the correlation between the measured parameters (i.e. CO2 flux and environmental parameters). The results of the statistical procedure showed that 1) the oscillation at low period (hours and days) are due to variations of environmental parameters and 2) the soil CO2 flux measured at Lipari is characterized also by long period variations (compared to the previous ones, i.e. weeks), difficult to explain just in terms of environmental parameters. The applied methodology, based on multivariate statistical techniques, has allowed to identify residual anomalies, following a suitable procedure to handle the multicollinearity. In the period from August 20 to August 23, 2014, when there were not both significant air pressure variations and rain events, anomalous positive residuals were identified. These residual anomalies are those interesting from volcanic surveillance point of view. However, to assess whether the residual anomalies are effectively controlled by variations induced by the deep system (i.e. seismic and/or volcanic activity) others parameters, such as wind speed and wind direction, and more time of observation should be taken into consideration. Highlights: A new unit for continuous monitoring of CO2 flux has been proposed. Data processing methodology for the identification of anomalies in CO2 flux time series is discussed. Field test was performed at the old kaolin quarry (Lipari) in which no monitoring activity is active. The variation of CO2 flux is generally correlated to Pair, Tair, rain and soil conductivity. CO2 flux variation triggered by deep component seems to be highlighted by the proposed data processing.