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Predictive Data Analysis of Air Quality to Reduce Health Risks


   School of Electronics, Electrical Engineering and Computer Science

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  Dr T Duong  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

In urban areas, poor air quality is one of the major concerns to societies and environmental risk to public health. Long-term exposure to air pollution can cause chronic conditions such as cardiovascular and respiratory diseases as well as lung cancer, leading to reduced life expectancy. According to the world health organization (WHO), 90% of global citizens lived in areas where the air quality exceeds the safe level. The source of air pollution includes transports, industry, and home emissions, as well as the non-negligible population density of the growing cities. In terms of particles and gasses, air pollution is a complex mix of particulate matter (PM) and nitrogen dioxide (NO2). A safe level of exposure is likely to bring health benefits.

With the advent of the Internet of things (IoT), cities are already installing air pollution sensors to monitor the quality levels at different locations inside the cities. Such data is valuable to do the predictive analysis and provide micro-level air quality information to individuals. Consequently, it will help and provide awareness to the individuals to be less exposed in the poor air quality region.

The air quality monitoring sensors generate a huge amount of data that has a great potential to process it using machine learning models. In this project, the air quality data is considered as a multivariate time series and processed using sequence modeling algorithms to predict air quality in the region. The modern machine learning approaches are able to process the long history which will be the driving factor to analyse the long-term data analysis. Consequently, it will improve the prediction capabilities. An application like weather prediction will be developed to provide real-time information to individuals.

Project Description:

Although some research attempts have been made towards the air quality analysis which is related to environmental sciences based on the longitudinal data. The objective of this research is to do predictive analysis from a machine learning perspective. The following objectives are set to be achieved during the Ph.D. duration.

  1. Formulate a novel and holistic predictive model for air quality analysis in urban areas.
  2. Analyse the publicly available datasets and perform the comparative analysis of developed machine learning (possibly deep learning sequence model) models with state-of-the-art techniques.
  3. Application development of a handheld device to facilitate the individuals about the poor air quality. Consequently, it will provide awareness about the air quality in societies.

Project Key Words: machine learning, AI, data analysis, air quality, healthcare

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.

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