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 Enviorment 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.
These models and data sources would be leveraged by the candidate to explore several research avenues which may include:
i.Enhancement of current real time, high resolution air pollution models through additional data sources
ii.Exploration of the use of low-cost sensing elements to provide data to augment pollution mapping models.
iii.Investigation into producing a predictive model to forecasting health impacts of localised pollution
iv.Approaches to model exposure of individuals over time though their location history
v.Examination of methods of transport and effect on exposure levels
This project will leverage unique resources provided by the partner organisations. Ulster University will provide embedded sensing technologies, computing infrastructure and physical laboratory environments present within the School of Computing. Belfast City Council will offer access to data from sensors embedded within its smart city fabric and a platform to extend and deploy additional sensing elements. The Urban Healthy Living project will provide space-based sensing data and an associated augmented model incorporating ground-based sensing elements. Several local health trusts will provide data and expertise to supplement findings. This project is aligned with a larger initiative which the partners have proposed.
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:
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 studentship grant (RTSG) allocation to help support the PhD researcher.
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 studentship grant (RTSG) allocation to help support the PhD researcher.
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 studentship grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 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 studentship 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
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Monday 18 February 2019
25 to 29 March 2019
The largest of Ulster's campuses
Monday 2 December 2019
Subject: Computer Science and Informatics
When applying for this PhD opportunity please quote reference number: