Institute for Transport Studies (ITS)

Characterising roadside pollutant concentrations with multi-resolution analysis and unsupervised classifiers

Supervisor: Dr Haibo Chen

Studies have demonstrated that many health problems (e.g. respiratory and cardiovascular) can be caused or worsened by exposure to air pollution on a day-to-day basis. Road traffic is the largest emission source of many harmful pollutants such as CO, NOx and primary PM10, some of which contribute to the formation of ozone and secondary particles. Whilst considerable efforts are being made by manufacturers to reduce vehicle emissions at source and by scientists to develop new technologies to exploit cleaner fuels, the Traffic Management Act puts in place the legislation that imposes a duty on local authorities, within the Local Transport Plans, to manage their traffic networks more efficiently and reduce traffic emissions.

To achieve this, many air quality monitoring stations have been established in the urban areas, especially at roadside. Large-scale pollution and traffic data have been collected and integrated with other types of data such as meteorological conditions for the analysis of their intrinsic relationships. However, such relationships are dynamic and complex due to a number of factors varying from time to time including vehicle fleet composition, fuel type, engine size, local topology and built environment. As a result, pollution hotspots and episodes are often produced but difficult to automatically detect in the time domain due to noise. This project is set to explore the use of time-frequency analysis techniques to transfer and analyse the time-series data into the space domain in which the noise in the data could be identified and removed.

The data needed for the proposed research will be provided from the instrumentation of two metropolitan sites in Leeds (one junction and one nearby section of road) funded by a HEFCE-SRIF award of £1 million. The state-of-the-art facility allows various aspects of traffic activity (flow, composition, vehicle fleet age, journey times & variability), local air pollution at multiple sites (NOx, O3, ultra-fine particulates) local meteorology to be measured.

Reference

Munir S., Chen H. and Ropkins K. (2013), "Quantifying temporal trends in ground level ozone concentration in the UK", Science of the Total Environment, 458-460 (2013), pp. 217-227.

DfT (2004), "The Future of Transport - a network for 2030".

Zito P., Chen H. and Bell M.C. (2008), "Predicting Roadside CO and NO2 Concentrations Using Instrumented City Facility and Neural Networks", IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 3, pp. 514-522.

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