Hybrid physical-statistical models for air quality prediction from traffic data

Lead Research Organisation: University of Bath
Department Name: Mathematical Sciences

Abstract

In the present study, we shall develop a hybrid physical-statistical model for air quality prediction. The research is a collaborative work between AVL and the EPSRC Centre for Doctoral Training Statistical Applied Mathematics (SAMBa). The project aims to combine deterministic and statistical modelling to forecast air pollution levels within a city. The focus is on air quality change resulting from traffic patterns, vehicle types, urban layout and meteorological conditions. The aim is to move towards incorporating real time data from traffic and pollution monitors to inform the forecast. It is also an aim to develop methods that can distinguish the relative impact of traffic from other sources of pollution. First, a physical and chemical model will be used to describe automobile traffic and pollutant concentrations. A statistical model will be used to analyse data provided by traffic and pollution monitoring sensors located in a city. Subsequently, the statistical model will be used to identify and account for deviations from the assumptions of the physical model as well as to calibrate the model parameters. Finally, the goal will be to develop a hybrid system where both physical and statistical models work together to improve forecasts. The supervisory team consists of Prof. Paul Milewski who is an expert on physical modelling of fluid and continuum processes, and Dr Theresa Smith who is an expert on spatial statistics. Together they span the expertise needed for the project. The AVL contact is Gerhard Schagerl who will provide guidance on the industrial impact of the project.

Planned Impact

The impact of the SAMBa CDT will occur principally through the following two pathways:

1. Direct engagement with industrial partners, leading to PhD projects that are collaborative with industry, and that are focussed on topics with direct industrial impact.

2. The production of PhD graduates with
(a) the mathematical, statistical and computational technical skill sets that have been identified as in crucial demand both by EPSRC and by our industrial partners, coupled to
(b) extensive experience of industrial collaboration.

The underlying opportunity that SAMBa provides is to train graduates to have the ability to combine complex models with 'big data'. Such people will be uniquely equipped to deliver impact: whether they continue with academic careers or move directly to posts in industry, through quantitative modelling, they will provide the information that gives UK businesses competitive advantages. Our industrial partners make it clear to us that competitiveness in the energy, manufacturing, service, retail and financial sectors is increasingly dependent on who can best and most quickly analyse the huge datasets made available by the present information revolution.

During their training as part of SAMBa, these students will have already gained experience of industrial collaboration, through their PhD projects and/or the Integrated Think Tanks (ITTs) that we propose, that will give all SAMBa students opportunities to develop these transferable skills. PhD projects that involve industrial collaboration, whether arising from ITTs or not, will themselves deliver economic and social benefits to UK through the private companies and public sector organisations with which SAMBa will collaborate.

We emphasise that Bath is at the forefront of knowledge transfer (KT) activities of the kind needed to translate our research into impact. Our KT agenda has recently been supported by KT Accounts and Impact Acceleration Accounts from EPSRC (£4.9M in total) and a current HEFCE HEIF allocation of £2.4M. Bath is at the forefront of UK activity in KTPs, having completed 150 and currently holding 16 KTP contracts worth around £2.5M.

The SAMBa ITTs are an exciting new mechanism through which we will actively look for opportunities to turn industrial links into research partnerships, supported in the design of these projects by the substantial experience available across the University.

More widely, we envisage impact stemming from a range of other activities within SAMBa:

- We will look to feed the results of projects involving ecological or epidemiological data directly into environmental and public health policy. We have done this successfully many times and have three REF Case Studies describing work of this nature.

- Students will be encouraged to make statistical tools available as open source software. This will promote dissemination of their research results, particularly beyond academia. There is plenty of recent evidence that such packages are taken up and used.

- Students will discuss how to use new media to promote the public understanding of science, for example contributing to projects such as Wikipedia.

- Students will be encouraged to engage in at least one outreach activity. Bath is well known for its varied, and EPSRC-supported, public engagement activities that include Royal Institution Masterclasses, coaching the UK Mathematics Olympiad team, and reaching 50 000 people in ten days with an exhibit at the Royal Society's 350th Anniversary Summer Exhibition in 2010.

Publications

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