Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential for Machine Learning to reproduce the behaviour of a chemical mechanism, yet with reduced computational expense.
STFC Capabilities used
Jan 2021 – Oct 2021
SAQN have awarded ten Scoping Studies to new collaborative teams, addressing different air quality challenges. With awards of up to £8,000 per project, these are small scale activities to test out an idea, with a view to developing the research further after the initial phase, and potentially exploring commercialisation. All the projects make innovative use of STFC capabilities, either applying new techniques to air quality research, or developing the capacity of STFC laboratories in new ways.