Comparative soil CO2 flux measurements and geostatistical estimation methods on Masaya volcano, Nicaragua

We present a comparative study of soil CO₂ flux () measured by five groups (Groups 1–5) at the IAVCEI-CCVG Eighth Workshop on Volcanic Gases on Masaya volcano, Nicaragua. Groups 1–5 measured using the accumulation chamber method at 5-m spacing within a 900 m² grid during a morning (AM) period. These measurements were repeated by Groups 1–3 during an afternoon (PM) period. Measured ranged from 218 to 14,719 g m⁻² day⁻¹. The variability of the five measurements made at each grid point ranged from ±5 to 167%. However, the arithmetic means of fluxes measured over the entire grid and associated total CO₂ emission rate estimates varied between groups by only ±22%. All three groups that made PM measurements reported an 8–19% increase in total emissions over the AM results. Based on a comparison of measurements made during AM and PM times, we argue that this change is due in large part to natural temporal variability of gas flow, rather than to measurement error. In order to estimate the mean and associated CO₂ emission rate of one data set and to map the spatial distribution, we compared six geostatistical methods: arithmetic and minimum variance unbiased estimator means of uninterpolated data, and arithmetic means of data interpolated by the multiquadric radial basis function, ordinary kriging, multi-Gaussian kriging, and sequential Gaussian simulation methods. While the total CO₂ emission rates estimated using the different techniques only varied by ±4.4%, the maps showed important differences. We suggest that the sequential Gaussian simulation method yields the most realistic representation of the spatial distribution of , but a variety of geostatistical methods are appropriate to estimate the total CO₂ emission rate from a study area, which is a primary goal in volcano monitoring research.Editorial responsibility: H Shinohara     

Time-window-based filtering method for near-surface detection of leakage from geologic carbon sequestration sites

We use process-based modeling techniques to characterize the temporal features of natural biologically controlled surface CO₂ fluxes and the relationships between the assimilation and respiration fluxes. Based on these analyses, we develop a signal-enhancing technique that combines a novel time-window splitting scheme, a simple median filtering, and an appropriate scaling method to detect potential signals of leakage of CO₂ from geologic carbon sequestration sites from within datasets of net near-surface CO₂ flux measurements. The technique can be directly applied to measured data and does not require subjective gap filling or data-smoothing preprocessing. Preliminary application of the new method to flux measurements from a CO₂ shallow-release experiment appears promising for detecting a leakage signal relative to background variability. The leakage index of ±2 was found to span the range of biological variability for various ecosystems as determined by observing CO₂ flux data at various control sites for a number of years.     

Dynamics of CO<Subscript>2</Subscript> fluxes and concentrations during a shallow subsurface CO<Subscript>2</Subscript> release

A field facility located in Bozeman, Montana provides the opportunity to test methods to detect, locate, and quantify potential CO₂ leakage from geologic storage sites. From 9 July to 7 August 2008, 0.3 t CO₂ day⁻¹ were injected from a 100-m long, ~2.5-m deep horizontal well. Repeated measurements of soil CO₂ fluxes on a grid characterized the spatio-temporal evolution of the surface leakage signal and quantified the surface leakage rate. Infrared CO₂ concentration sensors installed in the soil at 30 cm depth at 0–10 m from the well and at 4 cm above the ground at 0 and 5 m from the well recorded surface breakthrough of CO₂ leakage and migration of CO₂ leakage through the soil. Temporal variations in CO₂ concentrations were correlated with atmospheric and soil temperature, wind speed, atmospheric pressure, rainfall, and CO₂ injection rate.     

Multi-spectral imaging of vegetation for detecting CO<Subscript>2</Subscript> leaking from underground

Practical geologic CO₂ sequestration will require long-term monitoring for detection of possible leakage back into the atmosphere. One potential monitoring method is multi-spectral imaging of vegetation reflectance to detect leakage through CO₂-induced plant stress. A multi-spectral imaging system was used to simultaneously record green, red, and near-infrared (NIR) images with a real-time reflectance calibration from a 3-m tall platform, viewing vegetation near shallow subsurface CO₂ releases during summers 2007 and 2008 at the Zero Emissions Research and Technology field site in Bozeman, Montana. Regression analysis of the band reflectances and the Normalized Difference Vegetation Index with time shows significant correlation with distance from the CO₂ well, indicating the viability of this method to monitor for CO₂ leakage. The 2007 data show rapid plant vigor degradation at high CO₂ levels next to the well and slight nourishment at lower, but above-background CO₂ concentrations. Results from the second year also show that the stress response of vegetation is strongly linked to the CO₂ sink–source relationship and vegetation density. The data also show short-term effects of rain and hail. The real-time calibrated imaging system successfully obtained data in an autonomous mode during all sky and daytime illumination conditions.