Geospatial estimation of non-exhaust emissions using remote earth observation

This project is available from the academic year 2020/21 onwards.

Theme: Environmental Pollution

Primary Supervisor:

David Green

Division of Analytical, Environmental & Forensic Sciences, KCL

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Secondary Supervisor:

Sean Beevers

Division of Analytical, Environmental & Forensic Sciences, KCL

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Project Description:

Vehicle exhaust pollution has had substantial press coverage recently as a result of Dieselgate and increased evidence of a range of health end points. However, non-exhaust emissions such as brake and tyre wear are currently estimated to contribute 50% of vehicle emissions in London and the health impact of these particles, relative to exhaust emissions, remain uncertain. In the future, as exhaust emissions are abated, these sources will become more important for both environmental policy development and human health impact. In the developing countries the resuspension from unpaved roads will also be a significant factor.
This project has two principal aims:
1. Identifying vehicle movements road by road in global cities by combining unique global high resolution satellite imagery, provided by the CASE partner (TCarter), and analysed using the application of AI, supported by medical image processing of the KCL School of Biomedical Engineering & Imaging Sciences.
2. The measurement of detailed PM species (high time resolution application of XRF and aerosol mass spectrometry) in cities with a range of climatology to develop non-exhaust emission factors and apply these to vehicle activity from 1 to inform the assessment of population exposure.
It is an exciting opportunity to undertake an interdisciplinary CASE studentship project in an expanding and world-leading team.

Policy Impact of Research:

Non-exhaust emissions are currently uncontrolled and poorly quantified; this project has the potential to develop new assessment techniques to better quantify these globally to input into health impact studies and the formulation of abatement strategies for this poorly quantified source.

CASE Partner:


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