Postgraduate research opportunities

Detecting and characterising airborne microplastics and asbestos fibres through Machine Learning

The project will contribute to the ongoing efforts in monitoring and improving air quality through the application of machine learning and deep learning algorithms for detection and characterisation of airborne microplastics and asbestos fibres.

Number of places



7 May 2020


3 years


Students applying should have (or expect to achieve) a high 2.1 undergraduate degree in a relevant Engineering/science discipline, and must be highly motivated to undertake multidisciplinary research. 

Knowledge and/or experience in material characterisation, environmental engineering, data analytics, machine learning and programming skills (e.g. Python, Matlab, PyTorch/TensorFlow) are desirable.

Project Details

Anthropogenic air pollutants such as asbestos microfibres and microplastics remain a significant hazard to global health. Asbestos is a well-known airborne pollutant responsible for over 100,000 deaths globally every year. In contrast, microplastics have only recently become a global health concern and their effect on human and animal wellbeing and the environment are not yet fully understood. Common human exposure vectors to microplastics include drinking water, food and personal care products. However, a critical vector that has not been broadly explored is air inhalation, despite its potential to affect a wider population due the extensive presence of microplastics in the environment.

At present, there is a lack of monitoring techniques to detect and characterise microfibres in air through quick, cheap and reliable measurements. However, new advances in sensor technologies have the potential to enable closer monitoring of air quality and to provide new insights on the effect of air pollutants. Microfibre attributes such as material type, size and shape are key to assessing toxicity but are challenging to determine due to the microscopic size of the fibres. Furthermore, in the case of microplastics, the wide range and similarity of polymeric materials, their aging caused by photo-, bio- and physical degradation and their highly variable concentrations make them even more difficult to characterise.

The project will investigate the use of Machine Learning and Deep Learning to provide a quick and reliable method to detect and characterise microfibres such as microplastics and asbestos in air samples. Cutting-edge characterisation techniques including Raman and fluorescence microscopy will inform a model that extracts the most relevant features from more accessible and affordable imaging measurements. The model will be trained to discriminate microplastics and asbestos fibres from other microfibres and dust, while characterising individual fibres and assessing the overall toxicity of the air sample. The project will contribute to the ongoing efforts in air quality monitoring and will be potentially transferrable to the analysis of other exposure vectors such as water streams.

In addition to undertaking cutting edge research, students are also registered for the Postgraduate Certificate in Researcher Development (PGCert), which is a supplementary qualification that develops a student’s skills, networks and career prospects.

Funding Details

This PhD project is initially offered on a self-funding basis. However, excellent candidates will be considered for a University scholarship.


Contact us

Miss Ewa Kosciuk

+44(0) 141 548 2835

James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ

How to apply

Apply for this project here – please quote the project title in your application.

During the application you'll be asked for the following information and evidence uploaded to the application:

  • your full contact details
  • transcripts and certificates of all degrees
  • proof of English language proficiency if you are not from a majority English-speaking country as recognised by UKVI
  • two references, one of which must be academic. Please see our guidance on referees
  • funding or scholarship information
  • international students must declare any previous UK study

By filling these details out as fully as possible, you'll avoid any delay to your application being processed by the University.

Useful resources

Your application and offer

Application System Guide

Fees and funding