A Standardised Procedure for Estimating Greenhouse Gas Baselines Using Radon-222

Top-down verification methods are crucial for ensuring confidence in the bottom-up approaches used to report greenhouse gas emissions. These methods are reliant on robust baseline estimates, which can be calculated via several methods using a combination of meteorological data, transport models, and tracers such as CO and radon-222. In particular, high-quality radon measurements have been shown to reliably and consistently identify baseline airmasses across the globe, due to radon’s unique properties as a terrestrial tracer. However, the methodology used in this process differs between studies, as a result of variations in the location (e.g. remote, coastal, terrestrial), altitude, and atmospheric features of each observation site, as well as the sensitivity of the instruments available at the time/location.

This study aims to provide a universal procedure with which to calculate baseline estimates of greenhouse gases using radon, accounting for differences between stations. To evaluate and adjust this procedure, data from the Kennaook/Cape Grim (Tasmania), Mauna Loa (Hawaii), Jungfraujoch (Switzerland), Mace Head (Ireland) and Monte Cimone (Italy) observatories will be assessed, encompassing a range of locations and altitudes. This will include analysis of a variety of greenhouse gases, to understand whether alterations in the technique are required when estimating baselines of different gases and highlight how features such as low pollution spikes of N₂O or sudden pollution events of SF₆ influence our ability to estimate their baseline levels. Furthermore, using back trajectories obtained from the FLEXPART atmospheric dispersion model and high-frequency trace gas observations at each site, modelled baseline estimates will be derived to provide a direct comparison to the radon methodology. In doing so, this research will provide an unambiguous procedure for future baseline estimates, increasing the accessibility of this technique and improving comparability between studies.