Summary

Ambient air pollution is the world’s leading environmental health risk and a pervasive threat to urban healthy living (Brook et al., 2017; Wells et al., 2018). Globally, air pollution causes more than 33% of deaths from strokes, lung disease and chronic obstructive pulmonary disease and 25% of deaths from ischaemic heart disease. It is estimated that particulate air pollution causes at least 40,000 deaths per year in the United Kingdom and costs the national economy £16 billion annually (The Department for Environment Food and Rural Affairs, 2017).  Internationally, pollution quelling measures are unlikely to produce measurable effect before 2050 (Kitous et al., 2017). Consequently, a reduction in exposure to air pollutants is the recommended course of action for those at risk of exposure (Halliwell et al., 2015; Kim et al., 2015; Brook et al., 2017), such as in an urban environment. Pervasive computing solutions can be used to gather sensor data indicating pollution level factors within an urban environment (Kumar et al., 2015; Alsamhi et al., 2018). Further modelling can determine risk and exposure related to an individual occupying an area. The output from this model may then be leveraged to inform at risk individuals to circumvent harmful exposure.

There are several solutions which aspire to achieve this outcome, however, they have a range of detractors which include:

*Inability to accurately model air-based pollutants in a high-resolution manner

*Reliance on models which require expensive sensing solutions, limiting applicability

*Absence of algorithms to accurately route individuals’ paths in a manner which adequately avoids harmful pollutants

*Incorporation of models which do not adequately incorporate topographical elements and pollutant dispersal

This project aims to draw on novel approaches to high resolution, near real-time modelling of urban air pollution using combinations of data sources from ground and satellite-based sensors and computational modelling of pollution aggregation and dispersion in an urban landscape.

The successful candidate will leverage existing models and data sources in order to augment them using low-cost sensing equipment and create a framework that allows for the implementation of applications that will enhance the quality of life of individuals. This project will be developed in cooperation with the Belfast City Council which will offer access to data from sensors embedded within its smart city fabric and a platform to extend and deploy additional sensing elements as required by the project.


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Publications record appropriate to career stage
  • A comprehensive and articulate personal statement
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Profile picture of Xin Wei

I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".

Xin Wei - PhD in Computer Science and Informatics


Profile picture of Jyotsna Talreja Wassan

In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words

Jyotsna Talreja Wassan - PhD in Computer Science and Informatics