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Dr Farzana Rahman
Applications accepted all year round
Self-Funded PhD Students Only
About the Project
The research project aims to construct an autonomous computational system using purposeful AI (ML, Deep Learning) that continuously collects and analyses datasets from various sources (e.g. telemetric and historical), to support fast-paced data-driven decision-making to predict air pollution and inform policymakers to deliver clean air zone. We take motivation from the recent rise of global crises (i.e., COVID-19, adverse weather, mineral shortages, etc.) that have posed challenges to national and international decision-making bodies.
The project aims to predict air pollution in major European and Asian cities leveraging advanced Artificial Intelligence (AI) technology to inform flexible strategies ensuring the delivery of clean-air zones. By focusing on fine particulate matter (PM2.5, PM10), which is produced mainly by road transport, burning, construction, and cooking, the research will concentrate on the air pollutants that have the most significant impact on human health. Indeed, it significantly increases the risk of mortality from lung and heart diseases, especially for children and the elderly. By blending cutting-edge AI with environmental science, the project aligns with United Nations Sustainable Development Goals(SDG), particularly SDG 3: Good health and Wellbeing and SDG 13: Climate action, by paving the way for healthier, more sustainable and harmonious urban living.
The project team will include computer scientists from the School of Computer Science and Mathematics, Kingston University, an industrial collaborator (Technocommconsulting Ltd UK) who will resource the telemetric data via dynamic sensors, and experts from environment departments of Kingston University and local councils. The project activities will include research into AI/Deep learning method development, liaison within the research team and with external partners, and presentation of work at project meetings, technical sessions, and scientific meetings.
Applicants should have at least an Honours Degree with a 2.1 or above (or equivalent) in Computer Science, Physics or related disciplines. In addition, they should have excellent programming skills in Python in an academic or industry setup, a good mathematical background and an interest in machine learning and purposeful collaborative AI.
References
• Méndez, M., Merayo, M.G. & Núñez, M. Machine learning algorithms to forecast air quality: a survey. Artif Intell Rev (2023). https://doi.org/10.1007/s10462-023-10424-4
• Kumar, K., Pande, B.P. Air pollution prediction with machine learning: a case study of Indian cities. Int. J. Environ. Sci. Technol. 20, 5333–5348 (2023). https://doi.org/10.1007/s13762-022-04241-5
• Forlani, C., Bhatt, S., Cameletti, M., Krainski, E. and Blangiardo, M., 2020. A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA. Environmetrics, 31(8), p.e2644. Available at: https://doi.org/10.1002/env.2644
• Zhang, B., Rong, Y., Yong, R., Qin, D., Li, M., Zou, G. and Pan, J., 2022. Deep learning for air pollutant concentration prediction: A review. Atmospheric Environment, p.119347. Available at: https://doi.org/10.1016/j.atmosenv.2022.119347
• Kumar, K., Pande, B.P. Air pollution prediction with machine learning: a case study of Indian cities. Int. J. Environ. Sci. Technol. 20, 5333–5348 (2023). https://doi.org/10.1007/s13762-022-04241-5
• Forlani, C., Bhatt, S., Cameletti, M., Krainski, E. and Blangiardo, M., 2020. A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA. Environmetrics, 31(8), p.e2644. Available at: https://doi.org/10.1002/env.2644
• Zhang, B., Rong, Y., Yong, R., Qin, D., Li, M., Zou, G. and Pan, J., 2022. Deep learning for air pollutant concentration prediction: A review. Atmospheric Environment, p.119347. Available at: https://doi.org/10.1016/j.atmosenv.2022.119347
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