The analysis of NOx-ozone effects from optimised air-traffic using algorithmic climate change functions

The aviation industry is an essential contributor to total anthropogenic climate change, and the ever-growing demand for air transport requires serious attention. While efforts have been made to curb CO2 emissions, non-CO2 effects that are even more significant according to recent research have not been given enough attention. The EU Horizon 2020 project ClimOp steps in to follow a more holistic approach to tackling the climate impact of aviation using novel operational measures. One such measure is climate-optimised flight planning, where small deviations can be made in aircraft trajectories to minimise their overall climate impact. In order to achieve this, algorithmic Climate Change Functions (aCCFs) are used to estimate the climate impact of local non-CO2 effects such as Nitrogen oxide (NOx) emissions (via Ozone formation and Methane depletion), aviation water vapour, and contrails by using meteorological inputs directly. By plugging these functions directly into an aircraft trajectory optimisation module, climate sensitive regions are detected and avoided leading to climate optimised trajectories. While a preliminary verification conducted specifically for NOx-ozone aCCFs showed positive signs, this research entails a more detailed verification procedure, which provides a deeper insight into its capability in predicting the impact of aviation NOx emissions on atmospheric changes in ozone. Air traffic simulation is performed concerning a subset of one-day European flights on the specifically chosen days characterised by high variability of NOx-ozone aCCFs. The air traffic on these days is optimised for cost and climate. A subsequent chemistry simulation captures the NOx effects from these re-routing procedures, and the climate impact of both scenarios can thereby be directly compared. It is expected that the results will confirm the effectiveness of NOx-ozone aCCFs in producing climate-friendly trajectories.