PhD in Microenvironment Recognition and Exposure Quantification During Personal Sensing of Environmental Pollutants: A Machine Learning Algorithm and Toolkit Development

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/

For eligible successful applicants, the studentships comprises:

• An stipend for 3.5 years (currently £20,780 p.a. for 2026/27) in line with UK Research and Innovation rates
• Payment of university tuition fees
• The budget for project costs is £9,000 which can be used for computer, lab, and fieldwork costs necessary for you to conduct your research.
• There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training

Project Aims and Methods

Over the past decade, portable devices have advanced significantly, allowing measurement of personal environmental exposures (e.g., temperature, air pollutants). These devices generate rich datasets from diverse microenvironments, often accompanied by GPS trajectory data. However, analyses typically focus on broad summaries or limited microenvironments (e.g. traffic) because existing methods cannot fully capture the complexity.  

This PhD project asks: How can time–activity patterns be classified automatically at scale, and how do microenvironmental characteristics shape individual exposure? Example research directions include: 

1. Develop a novel microenvironment classification algorithm by extending existing methods 
2. Model indoor air quality levels in London microenvironments by combining machine learning (e.g. YOLO), previously collected dataset, geospatial data (e.g. OpenStreetMap, Kartaview) and secondary mobility dataset (e.g. Facebook API) 
3. Collect a validation dataset with activity diary, microenvironment, and exposure data 
4. Test, adapt and release the algorithm as open-source R package. 

The doctoral researcher will have scope to codesign the project and pursue new directions. Training will include: exposure science (Exeter supervisor), machine learning and software development (Cardiff co-supervisor), and data access and interpretation (Imperial and York). 

This interdisciplinary partnership combines expertise in environmental science, computer science, and geospatial analysis to develop state-of-the-art methods with real-world impact. 

Useful recruitment links:  

For information relating to the research project please contact the lead Supervisor via: h.zhang5@exeter.ac.uk  

Funding Comment

For eligible students the studentship will cover home tuition fees plus an annual tax-free stipend.

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