Skip to main content Skip to navigation

London air quality monitored

London air quality monitoring improved

Researchers have developed state-of-the-art machine learning models that establish real-time connected network of sensors, across London - enabling more effective air quality forecasting and modelling than is currently available.

Researchers from The Alan Turing Institute’s programme in Data-centric Engineering, in collaboration with the University of Warwick, are working with partners including the Greater London Authority, to develop machine learning algorithms, data science platforms and statistical methodology to better integrate and analyse this sensor data.

With a better understanding of air pollution in a complex urban environment like London, it’s possible to design better policy interventions, and hence improve urban quality of life and life expectancy.

The group has been exploring ways to better integrate sensory information from various air quality sensors which vary significantly in their characteristics.

Dr Theo Damoulas is Assistant Professor in Data Science at the University of Warwick, with a joint appointment in Statistics and Computer Science. He is also a Turing Fellow, and is leading this project. He commented:

“The group is handling more than 1TB of data sources, and that’s growing every minute,” Damoulas explains, “These capture various aspects of air in London from air pollution sensor measurements to traffic jams, weather and street canyons [the way air behaves in streets flanked by tall buildings]. We are developing algorithms that can deal with this variability, or ‘heterogeneity’”.

The project has benefitted from being part of navigation specialists Waze’s ‘Connected Citizens Program’, providing real-time traffic data which, in conjunction with TfL data being supplied by the GLA, allows for a more accurate picture of key areas and levels of pollution across London.