Alan Turing Institute to improve modelling of London air quality monitoring

The Mayor of London is working with the Alan Turing Institute to explore ways to improve the modelling of air quality by collating existing and new data sources and enhancing the way it is analysed.

The work with the Alan Turing Institute will complement the Mayor’s existing collaborations with institutions such as King’s College London, who work with boroughs and City Hall to undertake local air quality monitoring and to develop the London Atmospheric Emissions Inventory.

The two-year partnership is taking place as part of the Turing-Lloyd’s Register Foundation programme in data-centric engineering, which aims to use data science to transform the safety and efficiency of complex infrastructure systems. 

There are approximately 100 large air quality monitoring stations in London active at any time. However, the numbers and types of monitors and sensors are likely to increase significantly in the coming years, creating a need to set standards and develop flexible ways to incorporate new sources of data collection in City Hall’s air quality modelling and analysis work.

Alan Turing Institute researchers will work with City Hall to develop machine learning models that will enable better air quality forecasting and modelling. These could then further inform policy to make targeted interventions that reduce the levels of pollution in key areas and at key times.

The Mayor’s chief digital officer, Theo Blackwell, said: “Working with the Alan Turing Institute will continue our efforts to harness London’s world-class strengths in data science and innovation to clean up the air we breathe to make life better for all Londoners.”

Theo Damoulas, Turing Fellow and assistant Professor of Data Science at the University of Warwick, said: “We are very excited to be working closely with City Hall and bringing our data science expertise to bear on such an important matter for the life of Londoners. My group at the University of Warwick and the team from the Turing-Lloyd’s Register Foundation programme are looking forward to developing and deploying state of the art statistical and machine learning algorithms on the air quality sensor networks in order to extract knowledge, inform policy, and monitor interventions.”