• Alberta Environment and Protected Areas (AEPA, provincial government) and South African Consortium of Air Quality Monitoring (SACAQM, international government and industry) The project uses dynamical systems coupled to statistical inference and machine learning to analyze greenhouse gas (GHG) emissions due to methanogenic bacteria and archaea breaking down hydrocarbon pollutants from oil sands operations. It validates the models against a real-time monitoring system and assesses toxicity of the bacterial activity on model organisms to show how methanogens affect the functioning of ecosystems around the oil sands. (H. Wang lead)

• Wildfire Modelling BC Wildfire Service, ScotiaBank, and Wildfire Robotics These projects model fire risk in the wildland-urban interface in the presence of a changing climate and with evolving fire management practices.  Statistical modelling of extremes in precipitation and temperature as well as uncertainty quantification associated with differential equation-based fire spread models provides the quantitative focus. (J. Braun and J.R.J. Thompson leads)