Can artificial intelligence aid air-quality mitigation?

  • Supervisors: Dr David Topping
    Prof Hugh Coe
    Dr James Allan

  • External Supervisors:

  • Contact:

    David Topping [david.topping@manchester.ac.uk]

  • CASE Partner: Transport for Greater Manchester [TfGM], Defra

Application deadline: 30 May 2018

Introduction:

Air pollution is a key socio-environmental driver that now represents one of the biggest multidisciplinary challenges in science, society and the economy today.  Overall, it is estimated that pollution causes 16% of all deaths worldwide and about 9 million premature deaths. Poor air quality is estimated to cost the UK £20 billion/year and aerosols also provide an important vector for the transmission of disease. The Internet of things (IoT) boom has generated momentum in designing devices that bridge the gap between sparse monitoring site measurements and a city environment that might help citizens make informed decisions on adapting to varying air-quality. With this, a number of smart city demonstrator projects have been funded across the UK, and globally, with the aim to prime development of new ‘smart’ systems used in cities that can change the evolution and distribution of air-quality. Any prosed solution and the data on which it is based will be coupled using emerging machine learning methods. These proposed solutions need to be rigorously tested by a range of partners from both academia and industry.

Project Summary:

In this project you will work with a wide range of emerging machine learning methods to identify hidden patterns in data captured through smart-city platforms in Manchester in relation to both traffic flow and air-quality. This will include accessing data from both existing and emerging platforms in ongoing smart-city initiatives CityVerve and Triangulum, including the addition of low cost sensor solutions designed to increase the density of air-quality measurements.  The project can take this information in a number of directions, with the aim to evaluate proposed artificial intelligence (AI) driven solutions for future city planning.  This includes the opportunity to take existing regional air-quality modeling frameworks and evaluate the ability to augement their performance using the aforementioned data platforms. This also might include the opportunity to evaluate development of a hypothetical AI driven smart traffic system in Manchester and help assess associated costs of deployment. Through a joint partnership between Manchester University and the Alan Turing Institute of Data Science in London, you will have the opportunity to spend time liaising with world-class data scientists and engineers through your PhD.  This will supplement the expertise now offered by our local Data Science Institute.

Who are we looking for and why should they apply?

We are looking for someone who is enthusiastic to build and evaluate software. We are not expecting you to start with all the required skillsets. A PhD is also the chance to build on your undergraduate training, and we will ensure you have the opportunity to do that in order to meet the project goals. Your project partners and co-supervisors are all world leaders in their fields, giving you a rich environment across multiple disciplines, including experience of benefiting and working with those in industry through our project partner Transport for Greater Manchester. You will have the opportunity to travel whilst presenting your work on the international stage at both EU and US conferences.

References:

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