Developing computational methods for monitoring and quantifying greenhouse gas (GHG) emissions in agriculture and biogas production.
I work on the ” Tomography in the field – new monitoring method for the effects of farming management on the GHG balance in agriculture” project led by Professor Aku Seppänen. We develop improved methods for assessing GHG balances in agriculture and small-scale biogas production. Acquiring accurate information on the GHG balances in agriculture is essential for determining and verifying the effect of more sustainable farming practices, to aid in the green transition. Our method utilizes multi-beam open-path laser dispersion spectroscopy measurement technology to estimate GHG fluxes, and we use a Bayesian state-space framework to reconstruct emission rates as temporally evolving tomography images. With this approach, spatial variations of the land and the uncertainties related to the monitored area are incorporated into the estimation. My focus lies partly on the geometrical modeling of the areas of interest, incorporating structures (barns, buildings, production machinery, etc.) and the varying altitude of ground level, into meshes suitable for FEM approximations of GHG fluxes.
For agricultural applications, we consider estimating fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), while in biogas plants we consider methane and ammonia (NH3). Our research topics will involve optimizing the measurement setup for field applications and the computational modeling involved with estimating GHG emissions with the Bayesian state-space framework.
