Air pollution exposure not only leads to respiratory and cardiovascular diseases, but is also detrimental to cognitive abilities, mental health, and prenatal development. Thus, cities worldwide have invested in sophisticated air pollution monitoring systems to assess and reduce air pollution and its consequences. When excessive build-up of air contaminants occurs, emergency measures must be enacted to reduce human exposure and decrease pollution levels. Predicting such situations a few hours in advance is critical to prevent human health from being compromised. Herein, this project aims at developing novel AI-based approaches which, like long short-term memory networks that were designed to take advantage of time series data, are able to predict air pollutants based on recently collected readings, including meteorological ones. Experiments will be conducted on data captured by monitoring stations from the London Air Quality Network. It expected that predictions will support decision making to implement, for example, low-emission or clean air zones in major cities. This project focuses on analysing historical air quality data from major British cities and relevant research articles. By analysing full-text publications, the project will proceed to scrutinise the air quality index data from major cities in the United Kingdom. The impact of significant events or initiatives, such as transport-induced pollution and the COVID-19 lockdown, will be assessed to identify long-term trends. The anticipated outcome includes data supported recommendations for the implementation of low-emission and clean air zones in major cities.
I completed a BSc in Computer Science at Kingston University in 2022 followed by an MSc in Data Science, for which I received the Kevin Walsh scholarship. Currently, I'm pursuing a PhD focused on developing AI-driven predictive models to anticipate air pollution levels across UK cities. Alongside my research, I find joy in teaching as a TA, sharing my knowledge and enthusiasm with students. My goal is to provide decision-makers with actionable insights derived from data-driven approaches, facilitating the implementation of effective interventions for cleaner and healthier cities. This journey embodies my dedication to both research excellence and education, as I strive to make a positive impact through innovative solutions and teaching in the field of computer science.