AbstractAtmospheric CO2 perturbations from simulated leaks have been used to determine the minimum statistically significant emissions that can be detected above background concentrations using a single atmospheric station. The study uses high precision CO2 measurements from the Arcturus atmospheric monitoring station in the Bowen Basin, Australia. A statistical model of the observed CO2 signal was constructed, combining both a regression and a time series model. 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 KS test calculates the probability that the modelled leak perturbation could be caused by natural variation in the background. Using pre-whitened data and selecting optimum test conditions, minimum detectable leaks located 1km from the measurement station were estimated at 22 tpd for an area source of size 100 m x 100 m and 14 tpd for a point source at a KS cutoff defined by using the formal p-value of 0.05. These are very large leaks located only 1km from the station and have a high false alarm rate of 56%. An alternative p-value could be chosen to reduce the false alarm rate but then the minimum detectable leaks are larger. A long term, single measurement station monitoring program that is unconstrained by prior information on the possible direction or magnitude of a leak, and based solely on detection of perturbations of CO2 due to leakage above a (naturally noisy) background signal, is likely to take one or more years to detect leaks of the order of 10 kt p.a. The sensitivity of detection of a leak above a background signal could be greatly improved through the installation of additional atmospheric monitoring stations or through greater prior knowledge about the location and size of a suspected leak.