Authors / CoAuthors
Wilson, P. | Feitz, A.J. | Jenkins, C.J. | Berko, H. | Loh, Z. | Hibberd, M. | Etheridge, D.
Abstract
The potential for using a single high precision atmospheric station for detecting CO2 leaks has been investigated using a variety of statistical approaches. Geoscience Australia and CSIRO Marine and Atmospheric Research installed an atmospheric monitoring station, Arcturus, in the Bowen Basin, Australia, in 2010 and have collected over 3 years' worth of atmospheric concentration measurements. The facility is designed as a prototype remote baseline monitoring station that could be deployed in areas targeted for commercial scale geological storage of carbon dioxide. Two Picarro gas analysers are deployed in the station to continuously monitor CO2, CH4 and CO2 isotopes. An automated weather station and an eddy covariance flux tower have also been installed at the site. Atmospheric CO2 perturbations, from simulated leaks, have been modelled to determine the minimum statistically significant emissions that can be detected above background concentrations at Arcturus. CO2 leakage was simulated from January to December (2011) using a 3D-coupled prognostic meteorological and pollutant dispersion model (TAPM). Simulations were conducted for various locations, emission rates and distances (1-10 km) from the station. The simulated leaks were simulated using an area source (100 m x 100 m) and a point source located in the optimum wind direction (SSE), which showed the largest perturbation. To better understand the observed CO2 signal, a statistical model combining both a regression and time series model was constructed. The regression model is a time dependent generalised additive model relating the CO2 to other observed atmospheric variables (e.g. wind speed, temperature, humidity). It accounts for seasonal trends through the inclusion of dummy variables. The time series model is based on a seasonal auto-regressive integrated moving average (ARIMA) model, but with the additional complexity of allowing auto-regressive relationships to depend on the time of day. A non-parametric goodness of fit approach using the Kolmogorov-Smirnoff (KS) test was then used to test whether simulated perturbations can be detected against the modelled expected value of the background for certain hours of the day and for particular seasons. The developed regression model allows us to pre-whiten the CO2 time series. Pre-whitening reduces both the variance and skew of the marginal distribution of the signal. This improves the power of the Kolmogorov-Smirnoff (KS) test when attempting to detect simulated perturbations against the background signal. The KS test calculates the probability that the modelled leak perturbation could be caused by natural variation in the background. For hours between 10am and 2pm in the winter of 2011, minimum detectable leaks located 1km from the measurement station improve from 44 to 22 tpd for an area source and 33 to 14 tpd for a point source at a p-value of 0.05. These are very large leaks located only 1 km from the station. Additionally, this approach results in a high false alarm rate of 56%. An alternative p-value could be chosen to reduce the false alarm rate but the overall conclusion is the same. A long term, single measurement station monitoring program that is unconstrained by prior information on possible leaks, and based on detection of perturbations of CO2 alone due to leakage above a (noisy) background signal, is likely to take one or more years to detect leaks of the order of 10kt p.a.
Product Type
nonGeographicDataset
eCat Id
78800
Contact for the resource
Custodian
Owner
Custodian
Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
Canberra
ACT
2601
Australia
Keywords
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- External PublicationArticle
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- QLD
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Carbon Sequestration Science
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- Published_Internal
Publication Date
2014-01-01T00:00:00
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asNeeded
Topic Category
environment
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Unknown
Parent Information
Extents
[-24.0, -23.5, 148.0, 148.5]
Reference System
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Source Information
Energy Procedia; 12th International Conference on Greenhouse Gas Control Technologies, 2014, Austin, Texas Meta data documented in reports "Metadata report: Arcturus atmospheric greenhouse gas monitoring" (Geocat 79277) and "Installation Report for Arcturus (ARA): An inland baseline station for the continuous measurement of atmospheric greenhouse gases" (Geocat 73714)