2024-B39 Sensors and AI for low-cost source apportionment

Project highlights

Develop new low cost techniques for source apportionment of air pollution
Work in both the UK and East Africa
Work with industrial and governance stakeholders to achieve maximum impact

Overview

Air pollution is a global killer responsible for approximately 7 million premature deaths per year worldwide. Cities with healthy populations are more prosperous because the population’s life expectancy, quality of life, and productivity are all increased. Poor air quality acts as a brake on prosperity by increasing market costs through various mechanisms including higher expenditure on healthcare and loss of labour productivity.

Successful air quality management and control not only requires measurement of air pollution levels, but it also requires information on the sources of air pollution and their relative magnitudes and importance. Without this information, it is impossible to plan and enact cost-effective control measures. By achieving local source apportionment in a lower cost manner, this project will allow source apportionment to be used more widely for regulatory and compliance purposes both in the Global North and South.

This studentship will work towards providing important breakthroughs in the wider and more comprehensive use of source apportionment by utilizing the recent revolution in low-cost techniques both in hardware (sensors) and software (artificial intelligence). The ability of low-cost sensors to apportion air pollution from specific activities within complex real-world environments will be investigated. By allowing individual pollution sources to be pinpointed, the developed methodologies will significantly improve global prospects to reduce air pollution concentrations through the targeting of specific sources.

Figure 1: Annotated photograph of Kibera in Nairobi, highlighting where potential sources of air pollution could arise from. (Photo: Prof. Francis Pope).

Host

University of Birmingham

Theme

Climate and Environmental Sustainability

Supervisors

Project investigator

Francis Pope (University of Birmingham, [email protected])

Co-investigators

Roy Harrison (University of Birmingham, [email protected])

How to apply

Each host has a slightly different application process.
Find out how to apply for this studentship.
All applications must include the CENTA application form.
Choose your application route

Methodology

Low-cost source apportionment will be applied to regulatory important sites that are impacted upon by emissions from various sources. Various statistical and machine learning methods will be used to analyse data collected from the low cost instruments. This will allow for the effect of specific pollution sources at different sites to be assessed. It will also allow for the anticipated levels of pollution, which are dependent on meteorological conditions and temporal variations, to be determined. Results from low cost sensors will be compared to regulatory grade source apportionment to ensure robustness of results.

Field locations are available in both the UK and East Africa (via industrial, governmental and UN partners) to investigate and appraise the ability of low-cost techniques in source apportionment. The use of arrays of sensors across urban areas in combination with the developed techniques will allow for source triangulation of the location of emitters.

Training and skills

Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.

The student will be provided with full training on the modelling packages with which to derive emission factors, including R, Matlab and Arc GIS Pro. The candidate will be encouraged to attend modules from atmospheric science related MSc courses at the University of Birmingham. They will be trained in an interdisciplinary environment at the University of Birmingham, with colleagues whose interests span meteorology, climatology, atmospheric chemistry and air quality. The student will benefit enormously from synergies between the studentship and the wider team including industrial and governance colleagues in the UK and Africa.

Partners and collaboration

The student will benefit from extensive links between the project and industrial, governance and NGO partners that are based both in the UK and Africa. The project will benefit from the stakeholders involved with the ASAP East Africa(https://www.francispope.com/asap) and WM-AIR (https://wm-air.org.uk) projects. The student will also benefit from working with industrial partner Dustscan Ltd, who are involved with cognate projects with Professor Pope.

Further details

Further details on how to contact the supervisor for this project and how to apply for this project can be found here:

For any enquiries related to this project please contact Francis Pope, [email protected].

To apply to this project:

You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2024.
You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.
Please submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm. Please select the PhD Geography and Environmental Science (CENTA) 2024/25 Apply Now button. The CENTA application form 2024 and CV can be uploaded to the Application Information section of the online form. Please quote CENTA 2024-B39  when completing the application form.

Applications must be submitted by 23:59 GMT on Wednesday 10th January 2024.

Possible timeline

Year 1

Literature survey and review paper. Exploring source apportionment techniques and low cost air pollution sensors. Developing new technique to combine low cost source apportionment with three dimensional source triangulation. UK based measurement campaign. Attend national UK conference.

Year 2

Further development of source apportionment technique to allow for multiple sensor data. Use of machine learning techniques to provide source apportionment predictions. Africa based field campaign with associated stakeholder engagement in country.

Year 3

Data analysis and interpretation. Implementation of field data into model. Discussions and analysis will be finalized here, and final outputs will be published. Presentation of results at international conference e.g. AGU in San Francisco, USA. Thesis preparation and viva.