Improving Air Quality in Cities
Using data to forecast air pollution across London and inform policy interventions
Modern cities face constant challenges to public health, including air pollution and disease outbreaks such as COVID-19. In London alone, an estimated 9,000+ people die each year due to breathing in pollutants, and there are 30,000+ cases of coronavirus. Dr Theo Damoulas’ research aims to give the city’s authorities dynamic information on busyness (activity levels) and air quality in the capital’s streets to aid public health measures. By providing the latest data and patterns, Transport for London (TfL) has been able to widen pavements and close roads to vehicles to support social distancing measures. Meanwhile charting air pollution allows cyclists and people on foot to avoid problem areas, in addition to guiding targeted intervention strategies.
The Greater London Authority, Transport for London and public health bodies have access to a wide range of tools and data sources to monitor mobility, transportation, traffic activity, and air pollution. Networks of ground and mobile sensors, satellites, JamCam cameras and CCTV footage as well as public transit activity metrics produce large and diverse data sets with the potential to improve monitoring and understanding. Machine learning algorithms can be used to identify patterns in London’s busynesss and air pollution. However, these methods perform poorly for complex data, particularly with the strong spatial and temporal (space and time) factors seen in mobility, transportation, traffic activity, and air pollution data. The dilemma lies in adapting statistical tools to address large scale complex problems. When accomplished, this approach can be applied to an extensive variety of data-driven challenges which present themselves in any large metropolitan city.
Two approaches are combined to break down obstacles in the underlying data. The first relies on computer modelling which can adapt to both unexpected results and initial models that may not have been specified correctly. The second uses statistical sampling methods to reduce large problems into those that are smaller and more manageable.
When combined, these approaches address both the unpredictability of the data as well as its sheer size. Further tailoring is then used to create a data science platform to tackle the real-world scenarios of London’s busyness and air pollution. Results can then be widely communicated through an application programming interface which simplifies the installation of software and apps. These allow for both live-updates and detailed results to be communicated to citizens, Transport for London and the Greater London Authority.
Dr Damoulas and the team at the Alan Turing Institute have already developed an application programming interface to monitor London’s activity using the transport and road networks, and footfall in London. This interface is used by both TfL and the Greater London Authority to closely monitor busyness of the city’s streets and enable comparisons to normal levels of activity, supporting recovery from the COVID-19 pandemic. TfL uses the interface directly in their control room dashboard to understand busyness and dynamically manage roads, cycle lanes and pavements, accommodating social distancing measures.
The same statistical approach has also begun to provide a detailed image of London’s air quality. An app in development will produce up-to-date and local air pollution forecasts for people walking, running or cycling through London. This will allow them to change their route and avoid the worst polluted areas. Authorities in London now have accurate means to incorporate new sources of data in their air quality and activity level analyses, to compare different strategies which combat air pollution and COVID-19 transmission. Not only will this enable improved protection of public health, it will give better value for money in public spending and provide a better, safer course of action for the coronavirus pandemic. Warwick researchers are exploring the extension of the data-driven approaches system into further cities, including Newcastle and Sydney.