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Dear Colleagues,
I would appreciate it if you could pass on this NERC-funded PhD position in traffic/air quality modelling and big data to those you think might be eligible or interested.
All the best,
Zhaoya
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Topic: Real-time integrated modelling of transport-related air pollution in urban street networks – risk assessment and policy evaluation
Funding eligibility: Funding is available for UK residents (fees + stipend) and EU residents (fees only).
Application closing date: 13th March, 2017
Project description: As cities become increasingly congested with growing traffic, the increase in transport-related emissions has raised concerns over the risk on human health and urban environment. Street canyons are hotspots of traffic-related air pollution, because at this spatial level significant variation of traffic volumes and the pollutant transport to receptors (i.e. exposed population) are both at a time scale of minutes. Many emission pollutants are chemically reactive, and fast reactions can take place at second-to-minute timescales to generate secondary pollutants. As a result, the variation of urban air quality can only be understood by considering the combined effects of fleet composition, traffic-produced turbulence, street and building geometries, and meteorological conditions in real time (i.e., a time scale of minutes). Fundamental scientific questions remain, for example, how to model real-time dynamics of the cause-and-effect process from transport activity to distribution of air pollution within street canyons, and how to account for both the heterogeneous distribution of air pollution across an urban street network and its variation in abundance with time. Answers to these questions are of utmost importance to better assess individual or population exposure and the potential risks on human health.
To model this cause-and-effect chain, an integrated modelling approach is built upon bridging research on three complex systems: (i) dynamical changes in travel demand, (ii) vehicle emissions, and (iii) dispersion of air pollutants in street canyons. This approach is thus, not only able to provide detailed indication of air quality in street canyons, but also can identify street sections with high volumes of vulnerable travellers, such as pedestrians and cyclists. These together contribute to a better quantification of potential health risks that could impose on a population in question. Furthermore, travel demand changes respond to different policies, such as speed limit and road pricing in Clean Air Zones; the effects propagate from varying traffic to emissions and cause the change in pollution concentrations. It is through the causes and effects reflected by this model chain that transport policies aiming at risk mitigation can be evaluated in regards to their effectiveness in reducing adverse impacts on human health and environment.
This project inherently deals with issues of Big Data, risk and mitigation in the integrated modelling approach. PhD students will receive related training from three aspects: 1) urban transport and air quality modelling and applications, 2) programming, computing and data handling, and 3) uncertainty and risk related analysis.
Entry requirements: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Geoinformatics, GIScience/Geocompuation, Transport Planning, Civil Engineering, Meteorology, Geography, Environmental Science, Computer/Data Science or Urban Planning. Applicants with skills in quantitative modelling or C/C++/Java programming are preferred.
For further details: Please contact Dr Zhaoya Gong (Birmingham):
· Telephone: +44(0)1214144240
· Supervisory panel: Dr Zhaoya Gong (Birmingham); Dr Xiaoming Cai (Birmingham)
Industrial partners: Anne Shaw (Birmingham City Council) [log in to unmask]
Application procedure: http://www.dream-cdt.ac.uk/studying/application/
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Dr. Zhaoya Gong
Lecturer in Human Geography (Big Data)
School of Geography, Earth and Environmental Sciences
University of Birmingham, Birmingham, B15 2TT, UK
Tel: +44(0)1214144240