Urban roadside green furniture such as trees and hedges could efficiently reduce exposure to roadside pollutants. Their physical properties such as leaf surfaces can act as biological filters. Complementing the scope of EPSRC funded INHALE (Health assessment across biological length scales for personal pollution exposure and its mitigation) project (EP/T003189/1).
The objectives of this project are to investigate their influence of trees on air pollutants via adopting an integrated modelling approach including particle physics, deposition properties and health benefits of reduced concentrations on at-risk people such as asthmatics. The work will include simulation of several cases using CFD methods. The momentum drag and particle deposition sink terms will be employed in the simulation model and simulated results evaluate against the monitored results. Finally, this model will be utilised to achieve the prediction of particles dispersion and deposit in the urban area considering greening effects, and further propose evidence-based optimum dimensions of relevant planting schemes to reduce impact of particulate pollution on public health.
While the specific objectives will be defined in the beginning of the project, the broad objectives of the project will include: (1) Developing a CFD-based greening model that considers detailed particle physics and green infrastructure properties, to allow assessment of the trees/hedges under different configurations such as their efficacy for roadside schools and parks; (2) Long-term monitoring of experiment data that could allow validation of computational model and build understanding of the pollutant reductions and alteration in physicochemical properties of particles; and (3) Optimum dimensions (shape, height, width, leaf area density, vegetation types) of relevant planting schemes and presenting the project findings in a user-friendly tool to allow policy makers/relevant stakeholders.
The expected outcome will be a greening model that can allow prediction of diffusion and deposition of pollutants on green infrastructure and optimisation of various greening scenarios, and a user-friendly tool summarising the findings of the entire project for possible utilisation by stakeholders/interested users.
The PhD project will be supervised by Professor Prashant Kumar (https://www.surrey.ac.uk/people/prashant-kumar
), who is a founding Director of the Global Centre for Clean Air Research (GCARE; http://www.surrey.ac.uk/gcare
). He has extensive experience in air pollution and green infrastructure monitoring/modelling and particle dynamics modelling. The student will work in a team of post-doctoral/PhD researchers at the GCARE team. H/She will work closely with the GCARE and project collaborators from the Imperial College London.
MEng/MSc or BEng in Environmental/Chemical/Civil/ Mechanical/Aeronautical/Automotive/Fluid Mechanics/Particle Physics/ /Transportation Engineering/Science or relevant discipline with a UK equivalent 2:1 classification or above.
In addition to the academic qualifications listed above, skills in computational fluid dynamics modelling of air pollution using ANYSYS/OpenFoam, as well as background/interest in air pollution monitoring/particle physics/statistical data analysis, are desirable. Relevant work experience is also useful (please provide sufficient detail so that its relevance can be established).
The funding for this project is primarily available to citizens of UK/EU. Any interested overseas candidates need to cover the difference between the home and overseas fee difference by themselves. In case of exceptional overseas candidates, options to cover this difference may be explored.
How to apply:
Applicants should apply through the Civil and Environmental Engineering PhD course page: https://www.surrey.ac.uk/postgraduate/civil-and-environmental-engineering-phd
Applicants are required to include a cover letter explaining their interest in the project, a CV with relevant qualifications and prior expertise in areas relevant to the project, relevant transcripts and the names and contact details of two referees. At least one reference should be from an individual with good knowledge of the applicant’s academic record, especially in projects/dissertations.
IELTS requirements: 6.5 or above (or equivalent) with 6.0 in each individual category.
Please note that applications will be assessed on first come first basis periodically until end of January 2020.