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C404: Development of inverse modelling techniques to quantify emissions of air pollutants in developing megacities (Priority project with CASE partner) (Lead Supervisor: Rod Jones, Chemistry)

Supervisors: Rod Jones (Chemistry), Alex Archibald (Chemistry) and David Carruthers (Cambridge Environmental Research Consultants)

Importance of the area of research:

Poor air quality currently poses one of the greatest threats to human life, with every year more than 6 million people dying early globally. As a result billions of pounds have been spent to improve the situation. To some extent things are better in many places of the world than they could have been (Archibald et al., 2017), but in developing countries air pollution remains a disastrous problem which is expected to get worse in an increasingly urbanised world.

To develop strategies to improve air quality we require integration of observations of air pollutants, numerical models and governmental policy. This project will involve the collection and analysis of observations of air pollution in developing megacities and the use of inverse numerical modelling to help enable transformative solutions to the air pollution problem.

Project summary:

This is a co-funded project with Cambridge Environmental Consultants (CERC). ;This project will involve the collection and interpretation of air pollutant measurements using new low cost sensor networks (Heimann et al., 2015) and quantification of sources of emissions using inverse numerical modelling. CERC are world leaders in the development of air quality models (their models being used in large numbers of cities across the world) and we will work with them to develop new techniques to harness low cost sensor network observations to develop knowledge on the sources and magnitudes of emissions in urban environments. These emissions data will enable new scenarios to be developed to assess the impacts of mitigating emissions on air quality.

What the student will do:

The student will start by evaluating model simulations using current best guess emission inventories with data from a new generation of low cost high quality air quality monitoring instruments. The student will then develop new statistical techniques to combine the model and observation data and invert these to calculate best sets of the "true" emissions. These inverted emissions will then be compared against independent and new observations made as part of the project.

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.


World Heath Organisation 2014,

Archibald A.T., et al., "A world avoided: impacts of changes in anthropogenic emissions on the burden and effects of air pollutants in Europe and North America". Faraday Discuss., 200, 2017.

Heimann I., et al., "Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors". Atmos. Environ., 113, 2015.

Follow this link to find out about applying for this project.

Other projects available from the Lead Supervisor can be viewed here.

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